bugfixes: router stickness vs transaction workflow parameters

Documentacao/IMPLEMENTACAO_WORKFLOWS_TRANSACIONAIS.md

@@ -0,0 +1,64 @@
+# Implementação — workflows transacionais determinísticos
+
+## Entrega
+
+Foi adicionada ao `agent_framework_oci` uma capacidade opcional para executar transações multi-etapas como workflows determinísticos compilados em LangGraph.
+
+### Módulo novo
+
+`libs/agent_framework/src/agent_framework/workflows/`
+
+- `models.py`: contratos Pydantic e validação estrutural;
+- `repository.py`: resolução de versão ativa e leitura de YAML imutável;
+- `registry.py`: registro desacoplado de actions sync/async;
+- `runtime.py`: compilação, cache e execução do StateGraph;
+- `tool_executor.py`: integração com a política da tool;
+- `__init__.py`: API pública.
+
+### Política expandida
+
+`ToolPolicy` agora aceita:
+
+```yaml
+execution:
+  mode: direct_tool | workflow | agent
+  workflow: nome_do_workflow
+  version: active | 1
+```
+
+O default permanece `direct_tool`, preservando compatibilidade.
+
+### Configuração
+
+Foram adicionados:
+
+- `ENABLE_TRANSACTIONAL_WORKFLOWS=false`;
+- `WORKFLOWS_PATH=./workflows`.
+
+### Template
+
+Inclui um exemplo completo de devolução de pedido com:
+
+- confirmação e campos obrigatórios pela política;
+- workflow YAML versionado;
+- actions de domínio no backend;
+- bifurcação determinística baseada no resultado da validação.
+
+## Validação realizada
+
+- `tests/unit/test_tool_policies.py`: 4 testes aprovados;
+- compilação Python de framework, template e novos testes: aprovada;
+- o teste funcional novo do LangGraph foi criado, mas não pôde ser executado neste container porque `langgraph` não está instalado no ambiente. A dependência já está declarada no `pyproject.toml` do framework.
+
+## Escopo e segurança
+
+Esta entrega cria o motor e a integração de política. Para operações críticas em produção ainda é necessário conectar:
+
+- execution store persistente;
+- idempotência de negócio nas actions/APIs;
+- autorização por escopo;
+- telemetria IC/NOC específica de workflow;
+- compensação/Saga quando aplicável;
+- estratégia corporativa de timeout e retry.
+
+Esses itens foram explicitamente documentados para evitar a falsa impressão de que retry por si só garante segurança transacional.

Documentacao/Implementando_Basic_Auth.md

@@ -0,0 +1,987 @@
+# Implementando Basic Auth
+
+Para validar **todo o circuito com Basic Auth**, você precisa configurar três relações distintas:
+
+```text
+Cliente de teste
+   └─ Basic Auth A ─► Agent Gateway :8010
+                         └─ Basic Auth B ─► Agent Backend :8000
+                                                └─ Basic Auth C ─► MCP Gateway :8300
+```
+
+Há um detalhe importante: no pacote atual, a autenticação Basic já funciona para chamadas **de entrada**, mas os clientes internos ainda não enviam Basic Auth:
+
+* `Agent Gateway → Agent Backend` não envia credencial;
+* `Agent Backend → MCP Gateway` envia apenas Bearer Token.
+
+Portanto, para testar o circuito inteiro com Basic Auth, faça os dois pequenos ajustes de código descritos abaixo.
+
+---
+
+# 1. Preparar o ambiente
+
+Considere que o ZIP foi extraído em:
+
+```bash
+cd agent_framework_oci_authentication_v2_1
+```
+
+Crie um único ambiente virtual para facilitar o teste:
+
+```bash
+python -m venv .venv
+source .venv/bin/activate
+```
+
+No Windows PowerShell:
+
+```powershell
+python -m venv .venv
+.\.venv\Scripts\Activate.ps1
+```
+
+Instale o framework e as dependências dos três componentes:
+
+```bash
+pip install -U pip
+
+pip install -e ./libs/agent_framework
+
+pip install \
+  -r ./Tuning-Performance/Authentication/agent_template_backend_authentication/requirements.txt \
+  -r ./apps/agent_gateway/requirements.txt \
+  -r ./apps/mcp_gateway/requirements.txt
+```
+
+Confirme a importação:
+
+```bash
+python -c "from agent_framework.security import install_authentication; print('framework ok')"
+```
+
+---
+
+# 2. Criar três pares de Client ID e Secret
+
+Use credenciais diferentes para cada trecho. Para teste local:
+
+| Fluxo                   | Client ID            | Secret de teste             |
+| ----------------------- | -------------------- | --------------------------- |
+| Cliente → Agent Gateway | `tia-test`           | `TiaGateway-Test-2026!`     |
+| Agent Gateway → Backend | `agent-gateway-test` | `GatewayBackend-Test-2026!` |
+| Backend → MCP Gateway   | `agent-backend-test` | `BackendMcp-Test-2026!`     |
+
+Esses valores são apenas para ambiente local. Não os reutilize em produção.
+
+## Gerar os hashes
+
+O script está em:
+
+```text
+Tuning-Performance/Authentication/
+  agent_template_backend_authentication/
+    scripts/generate_secret_hash.py
+```
+
+Execute:
+
+```bash
+python Tuning-Performance/Authentication/agent_template_backend_authentication/scripts/generate_secret_hash.py \
+  --secret 'TiaGateway-Test-2026!'
+```
+
+Depois:
+
+```bash
+python Tuning-Performance/Authentication/agent_template_backend_authentication/scripts/generate_secret_hash.py \
+  --secret 'GatewayBackend-Test-2026!'
+```
+
+E:
+
+```bash
+python Tuning-Performance/Authentication/agent_template_backend_authentication/scripts/generate_secret_hash.py \
+  --secret 'BackendMcp-Test-2026!'
+```
+
+Você receberá três valores semelhantes a:
+
+```text
+pbkdf2_sha256:310000:<salt>:<digest>
+```
+
+Guarde-os temporariamente:
+
+```bash
+HASH_CLIENT_GATEWAY='pbkdf2_sha256:310000:...'
+HASH_GATEWAY_BACKEND='pbkdf2_sha256:310000:...'
+HASH_BACKEND_MCP='pbkdf2_sha256:310000:...'
+```
+
+O hash muda a cada execução porque o salt é aleatório. Isso é esperado.
+
+---
+
+# 3. Configurar o Agent Gateway
+
+Entre no diretório:
+
+```bash
+cd apps/agent_gateway
+```
+
+Copie o exemplo:
+
+```bash
+cp .env.example .env
+```
+
+Adicione ao final do `.env`:
+
+```env
+# Entrada: cliente/TIA -> Agent Gateway
+AGENT_GATEWAY_AUTH_ENABLED=true
+AGENT_GATEWAY_AUTH_MODE=basic
+AGENT_GATEWAY_AUTH_BASIC_CLIENT_ID=tia-test
+AGENT_GATEWAY_AUTH_BASIC_SECRET_HASH=COLE_AQUI_HASH_CLIENT_GATEWAY
+AGENT_GATEWAY_AUTH_BASIC_REALM=agent-gateway
+
+AGENT_GATEWAY_AUTH_PUBLIC_PATHS=/health,/docs,/openapi.json,/redoc
+AGENT_GATEWAY_AUTH_PUBLIC_PREFIXES=
+
+# Saída: Agent Gateway -> Agent Backend
+BACKEND_AUTH_MODE=basic
+BACKEND_AUTH_CLIENT_ID=agent-gateway-test
+BACKEND_AUTH_SECRET=GatewayBackend-Test-2026!
+```
+
+Não coloque aspas no `.env`:
+
+```env
+BACKEND_AUTH_SECRET=GatewayBackend-Test-2026!
+```
+
+O arquivo de backends já aponta o backend Contas para:
+
+```yaml
+contas:
+  url: http://localhost:8000
+```
+
+Arquivo:
+
+```text
+apps/agent_gateway/config/backends.yaml
+```
+
+Para este teste, mantenha apenas o backend `contas` ou force o backend no payload. Caso contrário, pedidos sobre ofertas e suporte podem ser roteados para portas em que nenhum backend está rodando.
+
+---
+
+# 4. Fazer o Agent Gateway enviar Basic Auth ao backend
+
+Abra:
+
+```text
+libs/agent_framework/src/agent_framework/global_supervisor/client.py
+```
+
+Substitua a classe `BackendClient` por uma versão que aceite autenticação Basic.
+
+No início do arquivo, adicione:
+
+```python
+import os
+```
+
+Altere o construtor:
+
+```python
+class BackendClient:
+    def __init__(
+        self,
+        timeout_seconds: float = 120.0,
+        basic_client_id: str | None = None,
+        basic_secret: str | None = None,
+    ):
+        self.timeout_seconds = timeout_seconds
+        self.basic_client_id = basic_client_id
+        self.basic_secret = basic_secret
+
+    def _auth(self) -> httpx.BasicAuth | None:
+        if self.basic_client_id and self.basic_secret:
+            return httpx.BasicAuth(
+                username=self.basic_client_id,
+                password=self.basic_secret,
+            )
+        return None
+```
+
+No método `call_message`, troque:
+
+```python
+resp = await client.post(url, json=payload)
+```
+
+por:
+
+```python
+resp = await client.post(
+    url,
+    json=payload,
+    auth=self._auth(),
+)
+```
+
+No método `health`, você pode manter `/health` público. Caso queira enviar autenticação também, use:
+
+```python
+resp = await client.get(url, auth=self._auth())
+```
+
+Agora abra:
+
+```text
+apps/agent_gateway/app/main.py
+```
+
+Adicione:
+
+```python
+import os
+```
+
+Troque:
+
+```python
+backend_client = BackendClient(
+    timeout_seconds=settings.BACKEND_TIMEOUT_SECONDS
+)
+```
+
+por:
+
+```python
+backend_client = BackendClient(
+    timeout_seconds=settings.BACKEND_TIMEOUT_SECONDS,
+    basic_client_id=os.getenv("BACKEND_AUTH_CLIENT_ID"),
+    basic_secret=os.getenv("BACKEND_AUTH_SECRET"),
+)
+```
+
+Isso implementa:
+
+```text
+Agent Gateway → Agent Backend
+Authorization: Basic base64(agent-gateway-test:GatewayBackend-Test-2026!)
+```
+
+---
+
+# 5. Configurar o Agent Backend autenticado
+
+Entre no diretório:
+
+```bash
+cd Tuning-Performance/Authentication/agent_template_backend_authentication
+```
+
+Copie o exemplo:
+
+```bash
+cp .env.example .env
+```
+
+Ajuste a seção de autenticação:
+
+```env
+# Entrada: Agent Gateway -> Agent Backend
+AGENT_AUTH_ENABLED=true
+AGENT_AUTH_MODE=basic
+AGENT_AUTH_BASIC_CLIENT_ID=agent-gateway-test
+AGENT_AUTH_BASIC_SECRET_HASH=COLE_AQUI_HASH_GATEWAY_BACKEND
+AGENT_AUTH_BASIC_REALM=agent-contas
+
+AGENT_AUTH_PUBLIC_PATHS=/health,/docs,/openapi.json,/redoc
+AGENT_AUTH_PUBLIC_PREFIXES=
+```
+
+Para usar o MCP Gateway:
+
+```env
+MCP_GATEWAY_ENABLED=true
+MCP_GATEWAY_URL=http://localhost:8300
+MCP_GATEWAY_TIMEOUT_SECONDS=60
+
+# Saída: Agent Backend -> MCP Gateway
+MCP_GATEWAY_AUTH_MODE=basic
+MCP_GATEWAY_BASIC_CLIENT_ID=agent-backend-test
+MCP_GATEWAY_BASIC_SECRET=BackendMcp-Test-2026!
+```
+
+Para evitar dependências externas durante o primeiro teste, configure também:
+
+```env
+LLM_PROVIDER=mock
+ENABLE_LANGFUSE=false
+ENABLE_ANALYTICS=false
+
+SESSION_REPOSITORY_PROVIDER=memory
+MEMORY_REPOSITORY_PROVIDER=memory
+CHECKPOINT_REPOSITORY_PROVIDER=memory
+CACHE_PROVIDER=memory
+USAGE_REPOSITORY_PROVIDER=memory
+```
+
+Os nomes exatos de alguns providers podem depender do arquivo de configuração atual do framework. Caso o `.env.example` já contenha valores locais ou mock, preserve-os.
+
+---
+
+# 6. Fazer o Backend enviar Basic Auth ao MCP Gateway
+
+Abra:
+
+```text
+libs/agent_framework/src/agent_framework/gateways/mcp_gateway_client.py
+```
+
+Substitua a implementação por:
+
+```python
+from __future__ import annotations
+
+import base64
+from typing import Any
+
+import httpx
+
+
+class MCPGatewayClient:
+    def __init__(
+        self,
+        base_url: str,
+        token: str | None = None,
+        timeout_seconds: int = 60,
+        auth_mode: str | None = None,
+        basic_client_id: str | None = None,
+        basic_secret: str | None = None,
+    ):
+        self.base_url = base_url.rstrip("/")
+        self.token = token
+        self.timeout_seconds = timeout_seconds
+        self.auth_mode = (auth_mode or "").strip().lower()
+        self.basic_client_id = basic_client_id
+        self.basic_secret = basic_secret
+
+    def _headers(self) -> dict[str, str]:
+        if (
+            self.auth_mode == "basic"
+            and self.basic_client_id
+            and self.basic_secret
+        ):
+            raw = f"{self.basic_client_id}:{self.basic_secret}".encode("utf-8")
+            encoded = base64.b64encode(raw).decode("ascii")
+            return {"Authorization": f"Basic {encoded}"}
+
+        if self.token:
+            return {"Authorization": f"Bearer {self.token}"}
+
+        return {}
+
+    async def list_tools(self) -> dict[str, Any]:
+        async with httpx.AsyncClient(
+            timeout=self.timeout_seconds
+        ) as client:
+            response = await client.get(
+                f"{self.base_url}/v1/tools",
+                headers=self._headers(),
+            )
+            response.raise_for_status()
+            return response.json()
+
+    async def invoke_tool(
+        self,
+        *,
+        tenant_id: str,
+        agent_id: str,
+        channel: str | None,
+        tool_name: str,
+        arguments: dict[str, Any] | None = None,
+        business_context: dict[str, Any] | None = None,
+        metadata: dict[str, Any] | None = None,
+    ) -> dict[str, Any]:
+        payload = {
+            "tenant_id": tenant_id,
+            "agent_id": agent_id,
+            "channel": channel,
+            "tool_name": tool_name,
+            "arguments": arguments or {},
+            "business_context": business_context or {},
+            "metadata": metadata or {},
+        }
+
+        async with httpx.AsyncClient(
+            timeout=self.timeout_seconds
+        ) as client:
+            response = await client.post(
+                f"{self.base_url}/v1/tools/{tool_name}/invoke",
+                json=payload,
+                headers=self._headers(),
+            )
+            response.raise_for_status()
+            return response.json()
+```
+
+Agora abra:
+
+```text
+libs/agent_framework/src/agent_framework/mcp/tool_router.py
+```
+
+Localize:
+
+```python
+MCPGatewayClient(
+    base_url=getattr(
+        settings,
+        "MCP_GATEWAY_URL",
+        "http://localhost:8300",
+    ),
+    token=getattr(settings, "MCP_GATEWAY_TOKEN", None),
+    timeout_seconds=getattr(
+        settings,
+        "MCP_GATEWAY_TIMEOUT_SECONDS",
+        settings.MCP_TOOL_TIMEOUT_SECONDS,
+    ),
+)
+```
+
+Altere para:
+
+```python
+MCPGatewayClient(
+    base_url=getattr(
+        settings,
+        "MCP_GATEWAY_URL",
+        "http://localhost:8300",
+    ),
+    token=getattr(settings, "MCP_GATEWAY_TOKEN", None),
+    timeout_seconds=getattr(
+        settings,
+        "MCP_GATEWAY_TIMEOUT_SECONDS",
+        settings.MCP_TOOL_TIMEOUT_SECONDS,
+    ),
+    auth_mode=getattr(
+        settings,
+        "MCP_GATEWAY_AUTH_MODE",
+        None,
+    ),
+    basic_client_id=getattr(
+        settings,
+        "MCP_GATEWAY_BASIC_CLIENT_ID",
+        None,
+    ),
+    basic_secret=getattr(
+        settings,
+        "MCP_GATEWAY_BASIC_SECRET",
+        None,
+    ),
+)
+```
+
+Adicione estes campos em:
+
+```text
+libs/agent_framework/src/agent_framework/config/settings.py
+```
+
+Próximo das configurações existentes de MCP Gateway:
+
+```python
+MCP_GATEWAY_AUTH_MODE: str | None = None
+MCP_GATEWAY_BASIC_CLIENT_ID: str | None = None
+MCP_GATEWAY_BASIC_SECRET: str | None = None
+```
+
+Há também uma factory local em:
+
+```text
+Tuning-Performance/Authentication/
+  agent_template_backend_authentication/
+    app/mcp_gateway_client_factory.py
+```
+
+Ajuste para:
+
+```python
+from __future__ import annotations
+
+import os
+
+from agent_framework.gateways import MCPGatewayClient
+
+
+def build_mcp_gateway_client() -> MCPGatewayClient | None:
+    if os.getenv("MCP_GATEWAY_ENABLED", "true").lower() != "true":
+        return None
+
+    return MCPGatewayClient(
+        base_url=os.getenv(
+            "MCP_GATEWAY_URL",
+            "http://localhost:8300",
+        ),
+        token=os.getenv("MCP_GATEWAY_TOKEN") or None,
+        timeout_seconds=int(
+            os.getenv("MCP_GATEWAY_TIMEOUT_SECONDS", "60")
+        ),
+        auth_mode=os.getenv("MCP_GATEWAY_AUTH_MODE"),
+        basic_client_id=os.getenv(
+            "MCP_GATEWAY_BASIC_CLIENT_ID"
+        ),
+        basic_secret=os.getenv(
+            "MCP_GATEWAY_BASIC_SECRET"
+        ),
+    )
+```
+
+---
+
+# 7. Configurar o MCP Gateway
+
+Entre no diretório:
+
+```bash
+cd apps/mcp_gateway
+```
+
+Crie `.env`:
+
+```bash
+cp .env.example .env
+```
+
+Adicione:
+
+```env
+# Entrada: Agent Backend -> MCP Gateway
+MCP_GATEWAY_AUTH_ENABLED=true
+MCP_GATEWAY_AUTH_MODE=basic
+MCP_GATEWAY_AUTH_BASIC_CLIENT_ID=agent-backend-test
+MCP_GATEWAY_AUTH_BASIC_SECRET_HASH=COLE_AQUI_HASH_BACKEND_MCP
+MCP_GATEWAY_AUTH_BASIC_REALM=mcp-gateway
+
+MCP_GATEWAY_AUTH_PUBLIC_PATHS=/health,/ready,/docs,/openapi.json,/redoc
+MCP_GATEWAY_AUTH_PUBLIC_PREFIXES=
+
+MCP_GATEWAY_CONFIG_PATH=config/mcp_gateway.yaml
+```
+
+## Desabilitar o mecanismo Bearer legado
+
+O MCP Gateway ainda possui um segundo mecanismo antigo, configurado dentro de:
+
+```text
+apps/mcp_gateway/config/mcp_gateway.yaml
+```
+
+Localize a seção:
+
+```yaml
+auth:
+  enabled: true
+```
+
+Altere para:
+
+```yaml
+auth:
+  enabled: false
+```
+
+Isso é necessário porque o novo middleware já faz a autenticação Basic. Caso o `auth_check()` legado continue habilitado, a requisição passará pelo Basic e depois será rejeitada por não possuir Bearer Token.
+
+---
+
+# 8. Subir os componentes
+
+Use quatro terminais.
+
+## Terminal 1 — MCP Servers
+
+O MCP Gateway precisa ter pelo menos um servidor MCP disponível para demonstrar uma chamada real.
+
+Na raiz do projeto:
+
+```bash
+source .venv/bin/activate
+```
+
+Suba o servidor telecom:
+
+```bash
+uvicorn mcp.servers.telecom_mcp_server.main:app \
+  --host 0.0.0.0 \
+  --port 8100 \
+  --reload
+```
+
+Em outro terminal, caso queira também o retail:
+
+```bash
+uvicorn mcp.servers.retail_mcp_server.main:app \
+  --host 0.0.0.0 \
+  --port 8200 \
+  --reload
+```
+
+Confira as URLs configuradas em:
+
+```text
+apps/mcp_gateway/config/mcp_gateway.yaml
+```
+
+Para execução local, devem apontar para:
+
+```yaml
+url: http://localhost:8100
+```
+
+e:
+
+```yaml
+url: http://localhost:8200
+```
+
+---
+
+## Terminal 2 — MCP Gateway
+
+```bash
+cd apps/mcp_gateway
+source ../../.venv/bin/activate
+```
+
+Suba usando `--env-file`. Isso é importante porque o middleware lê variáveis com `os.getenv()`:
+
+```bash
+uvicorn app.main:app \
+  --host 0.0.0.0 \
+  --port 8300 \
+  --reload \
+  --env-file .env
+```
+
+Teste a saúde pública:
+
+```bash
+curl http://localhost:8300/health
+```
+
+Teste um endpoint protegido sem credencial:
+
+```bash
+curl -i http://localhost:8300/v1/tools
+```
+
+Esperado:
+
+```text
+HTTP/1.1 401 Unauthorized
+```
+
+Teste com Basic Auth:
+
+```bash
+curl -i \
+  -u 'agent-backend-test:BackendMcp-Test-2026!' \
+  http://localhost:8300/v1/tools
+```
+
+Esperado:
+
+```text
+HTTP/1.1 200 OK
+```
+
+---
+
+## Terminal 3 — Agent Backend
+
+```bash
+cd Tuning-Performance/Authentication/agent_template_backend_authentication
+source ../../../.venv/bin/activate
+```
+
+Suba:
+
+```bash
+uvicorn app.main:app \
+  --host 0.0.0.0 \
+  --port 8000 \
+  --reload \
+  --env-file .env
+```
+
+Teste saúde:
+
+```bash
+curl http://localhost:8000/health
+```
+
+Teste endpoint protegido sem credencial:
+
+```bash
+curl -i http://localhost:8000/agents
+```
+
+Esperado:
+
+```text
+HTTP/1.1 401 Unauthorized
+```
+
+Teste com a credencial usada pelo Agent Gateway:
+
+```bash
+curl -i \
+  -u 'agent-gateway-test:GatewayBackend-Test-2026!' \
+  http://localhost:8000/agents
+```
+
+Esperado:
+
+```text
+HTTP/1.1 200 OK
+```
+
+Teste mensagem diretamente:
+
+```bash
+curl -X POST http://localhost:8000/gateway/message \
+  -u 'agent-gateway-test:GatewayBackend-Test-2026!' \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "channel": "web",
+    "agent_id": "telecom_contas",
+    "tenant_id": "default",
+    "payload": {
+      "text": "Quero consultar minha fatura",
+      "session_id": "teste-backend-001",
+      "user_id": "user-001",
+      "customer_id": "12345",
+      "message_id": "msg-001"
+    }
+  }'
+```
+
+---
+
+## Terminal 4 — Agent Gateway
+
+```bash
+cd apps/agent_gateway
+source ../../.venv/bin/activate
+```
+
+Suba:
+
+```bash
+uvicorn app.main:app \
+  --host 0.0.0.0 \
+  --port 8010 \
+  --reload \
+  --env-file .env
+```
+
+Teste saúde:
+
+```bash
+curl http://localhost:8010/health
+```
+
+Teste endpoint protegido sem credencial:
+
+```bash
+curl -i http://localhost:8010/backends
+```
+
+Esperado:
+
+```text
+HTTP/1.1 401 Unauthorized
+```
+
+Teste com a credencial externa:
+
+```bash
+curl -i \
+  -u 'tia-test:TiaGateway-Test-2026!' \
+  http://localhost:8010/backends
+```
+
+Esperado:
+
+```text
+HTTP/1.1 200 OK
+```
+
+---
+
+# 9. Validar o circuito completo
+
+Force o backend `contas` para evitar que o roteador selecione um backend não iniciado:
+
+```bash
+curl -X POST http://localhost:8010/gateway/message \
+  -u 'tia-test:TiaGateway-Test-2026!' \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "channel": "web",
+    "backend_id": "contas",
+    "tenant_id": "default",
+    "agent_id": "telecom_contas",
+    "session_id": "circuito-basic-001",
+    "payload": {
+      "text": "Quero consultar minha fatura",
+      "session_id": "circuito-basic-001",
+      "user_id": "user-001",
+      "customer_id": "12345",
+      "message_id": "msg-circuito-001"
+    }
+  }'
+```
+
+O circuito esperado é:
+
+```text
+curl
+  │ Basic tia-test
+  ▼
+Agent Gateway :8010
+  │ Basic agent-gateway-test
+  ▼
+Agent Backend :8000
+  │ Basic agent-backend-test
+  ▼
+MCP Gateway :8300
+  ▼
+MCP Server :8100 ou :8200
+```
+
+---
+
+# 10. Como comprovar cada autenticação
+
+Faça testes negativos em cada trecho.
+
+## Secret externo incorreto
+
+```bash
+curl -i \
+  -u 'tia-test:senha-errada' \
+  http://localhost:8010/backends
+```
+
+Resultado esperado:
+
+```text
+401 Unauthorized
+```
+
+## Secret do gateway para backend incorreto
+
+Altere temporariamente no `apps/agent_gateway/.env`:
+
+```env
+BACKEND_AUTH_SECRET=senha-errada
+```
+
+Reinicie o Agent Gateway e envie uma mensagem.
+
+O gateway deverá retornar erro de backend, normalmente:
+
+```text
+502 Bad Gateway
+```
+
+O erro interno será originado por um:
+
+```text
+401 Unauthorized
+```
+
+do Agent Backend.
+
+## Secret do backend para MCP incorreto
+
+Altere temporariamente:
+
+```env
+MCP_GATEWAY_BASIC_SECRET=senha-errada
+```
+
+Reinicie o backend e execute uma frase que acione uma ferramenta MCP.
+
+O backend deverá registrar falha na chamada ao MCP Gateway com:
+
+```text
+401 Unauthorized
+```
+
+---
+
+# 11. Verificação rápida de portas
+
+No Linux ou WSL:
+
+```bash
+ss -lntp | grep -E ':8000|:8010|:8100|:8200|:8300'
+```
+
+No Windows PowerShell:
+
+```powershell
+Get-NetTCPConnection -State Listen |
+  Where-Object LocalPort -in 8000,8010,8100,8200,8300 |
+  Sort-Object LocalPort
+```
+
+Você deverá ver:
+
+```text
+8000  Agent Backend
+8010  Agent Gateway
+8100  Telecom MCP Server
+8200  Retail MCP Server
+8300  MCP Gateway
+```
+
+## Observação importante
+
+O segredo original precisa existir no componente cliente:
+
+```text
+TIA ou curl:
+  TiaGateway-Test-2026!
+
+Agent Gateway:
+  GatewayBackend-Test-2026!
+
+Agent Backend:
+  BackendMcp-Test-2026!
+```
+
+Os componentes servidores armazenam apenas os hashes:
+
+```text
+Agent Gateway:
+  hash de TiaGateway-Test-2026!
+
+Agent Backend:
+  hash de GatewayBackend-Test-2026!
+
+MCP Gateway:
+  hash de BackendMcp-Test-2026!
+```
+
+Em produção, os segredos originais e hashes devem vir de Vault ou Kubernetes Secret, não de arquivos `.env`.

Documentacao/RELEASE_NOTES_GENERIC_DETERMINISTIC_INTENT_SHIFT_V15.md

@@ -0,0 +1,47 @@
+# Release Notes — Generic Deterministic Intent Shift v15
+
+## Problema corrigido
+
+A Route Stickiness podia preservar a intent anterior quando a nova mensagem correspondia a uma intent configurada no `routing.yaml`, mas a frase do usuário omitia conectores curtos presentes na keyword configurada.
+
+Exemplo real de configuração:
+
+- keyword: `qual é o meu plano`
+- mensagem: `qual o meu plano`
+
+A classificação determinística não reconhecia a nova intent e a continuity acabava mantendo a intent anterior.
+
+## Correção
+
+O `EnterpriseRouter` continua usando, nesta ordem:
+
+1. match exato;
+2. sequência completa de tokens com palavras inseridas (`ordered_tokens`);
+3. sequência de tokens informativos tolerando a omissão de conectores curtos presentes na keyword (`ordered_content_tokens`).
+
+A terceira estratégia ignora, somente no lado da keyword, tokens de até dois caracteres e exige pelo menos dois tokens informativos. Não há nomes de intents, agentes, domínios ou verbos de negócio hardcoded.
+
+Assim, a solução é dirigida integralmente pelas intents carregadas do `routing.yaml` da aplicação.
+
+## Precedência sobre Route Stickiness
+
+Quando o candidato determinístico encontrado é diferente da intent ativa, ele preempta a stickiness e retorna:
+
+- `route_stickiness_preempted: true`
+- `previous_agent`
+- `previous_intent`
+- `keyword_match_strategy`
+
+A continuity LLM não é chamada nesse caminho.
+
+## Casos cobertos
+
+### Mesmo agente, nova intent
+
+`retail_order_tracking` -> `quero cancelar meu pedido` -> `retail_order_cancel`
+
+### Mesmo agente, tools diferentes
+
+`contas_invoice_query` -> `qual o meu plano` -> `contas_plan_information`
+
+Mesmo que ambas as intents usem `faturas_agent`, as tools mudam de `consultar_faturas` para `consultar_plano`.

Documentacao/RELEASE_NOTES_ROUTE_STICKINESS_DETERMINISTIC_INTENT_SHIFT_V14.md

@@ -0,0 +1,41 @@
+# Route Stickiness — deterministic intent shift (v14)
+
+## Problema corrigido
+
+Uma keyword multi-token como `cancelar pedido` não era reconhecida em frases como `quero cancelar meu pedido`. O match legado usava substring literal; assim, a keyword genérica `pedido` podia manter `retail_order_tracking` e a continuidade reutilizava a intent anterior.
+
+## Correção
+
+O `EnterpriseRouter` agora possui um segundo estágio determinístico para keywords multi-token: ordered-token matching com até três tokens intermediários. Não há chamada adicional de LLM.
+
+Exemplos reconhecidos pela keyword configurada `cancelar pedido`:
+
+- `quero cancelar meu pedido`
+- `quero cancelar o meu pedido`
+- `pode cancelar esse pedido`
+- `gostaria de cancelar meu pedido`
+
+Quando esse match identifica uma intent diferente da ativa, ele preempta a route stickiness antes do LLM de continuidade.
+
+Metadados de auditoria esperados:
+
+```json
+{
+  "method": "keyword",
+  "intent": "retail_order_cancel",
+  "metadata": {
+    "matched_keyword": "cancelar pedido",
+    "keyword_match_strategy": "ordered_tokens",
+    "route_stickiness_preempted": true,
+    "previous_intent": "retail_order_tracking"
+  }
+}
+```
+
+## Custo de LLM
+
+Para mudança explícita reconhecida deterministicamente, o classificador LLM de continuity não é chamado. Para mensagens sem sinal explícito, a Route Stickiness continua com o comportamento configurado.
+
+## Regressão
+
+Testes cobrem mudança `retail_order_tracking -> retail_order_cancel` no mesmo `orders_agent`, inclusive com palavras intermediárias. A suíte relacionada passou com 18 testes.

FIX_TRANSACTION_INTENT_LOOP.md

@@ -0,0 +1,30 @@
+# Correção: escape de estado transacional / mudança de intenção
+
+Correção aplicada em 2026-08-20 para impedir que uma sessão fique presa em `COLLECTING_PARAMETERS` ou `AWAITING_CONFIRMATION` quando o usuário muda explicitamente de assunto.
+
+## Comportamento corrigido
+
+Antes:
+
+1. uma transação entrava em `COLLECTING_PARAMETERS`;
+2. `next_state` forçava o mesmo agente via `state_policies`;
+3. toda mensagem seguinte era tratada como tentativa de preencher o parâmetro faltante;
+4. uma nova intenção como `quais sao meus servicos` permanecia presa no fluxo anterior.
+
+Agora:
+
+- o `EnterpriseRouter` verifica mudança explícita de intenção antes de aplicar o lock de estado;
+- keyword explícita tem prioridade;
+- quando necessário, o LLM router pode detectar mudança com confiança >= `router.confidence_threshold`;
+- a decisão recebe `metadata.transaction_interruption=intent_shift`;
+- o runtime encerra a transação pendente como `CANCELLED`, limpa `next_state`, parâmetros e latches, e prossegue com a nova intent;
+- cancelamentos explícitos como `cancele essa operação anterior` funcionam também durante `COLLECTING_PARAMETERS`.
+
+## Testes adicionados
+
+- mudança de intent durante `COLLECTING_PARAMETERS`;
+- resposta curta/baixa confiança permanece na transação;
+- cancelamento explícito durante coleta de parâmetros;
+- limpeza do estado transacional antes de executar a nova intent.
+
+Testes focados: 19 passed.

FIX_TRANSACTION_PARAMETER_PRECEDENCE.md

@@ -0,0 +1,32 @@
+# Transaction parameter precedence fix
+
+Correção para a regressão em que uma resposta curta que preenchia um parâmetro pendente (ex.: `R$ 71,99` para `valor`) era classificada pelo LLM Router como uma nova intent e interrompia a transação.
+
+## Regra aplicada
+
+Durante `COLLECTING_PARAMETERS` a ordem passa a ser:
+
+1. resposta compatível com parâmetro pendente -> mantém a política de estado e continua a transação;
+2. cancelamento explícito -> cancela a transação;
+3. nova intenção clara/pergunta explícita -> interrompe a transação e volta ao roteamento normal;
+4. caso ambíguo -> permanece em clarificação.
+
+Exemplo corrigido:
+
+```text
+não fiz essa contratação TIM CTRL Redes Sociais 8.0
+-> informe valor
+R$ 71,99
+-> valor=71.99; continua contestar_cobranca; executa pré-validação
+```
+
+A mensagem `R$ 71,99` não pode mais virar `contas_invoice_explanation` enquanto `valor` estiver pendente.
+
+## Testes
+
+Foram adicionados testes para:
+
+- valor monetário com LLM sugerindo outra intent;
+- entidade curta como resposta de parâmetro;
+- pergunta clara durante coleta ainda interrompendo a transação;
+- regressões existentes de intent-shift e transactional tool flow.

Tuning-Performance/Authentication/agent_template_backend_authentication/app/agents/runtime.py

@@ -3,5 +3,8 @@ from __future__ import annotations
 # Compatibilidade local do template/backend.
 # A implementação oficial agora fica no framework para evitar duplicação entre agentes.
 from agent_framework.runtime import AgentRuntimeMixin, MessageBuilder, RuntimeContext
+from app.presentation import register_tool_renderers
+
+register_tool_renderers()
 
 __all__ = ["AgentRuntimeMixin", "MessageBuilder", "RuntimeContext"]

Tuning-Performance/Authentication/agent_template_backend_authentication/app/presentation/__init__.py

@@ -0,0 +1,3 @@
+from .tool_renderers import register_tool_renderers
+
+__all__ = ["register_tool_renderers"]

Tuning-Performance/Authentication/agent_template_backend_authentication/app/presentation/tool_renderers.py

@@ -0,0 +1,74 @@
+from __future__ import annotations
+
+from typing import Any
+
+from agent_framework.presentation import register_tool_response_renderer
+
+
+def _money_brl(value: Any) -> str:
+    try:
+        return f"{float(value):.2f}".replace(".", ",")
+    except (TypeError, ValueError):
+        return str(value)
+
+
+def render_telecom_invoice(*, tool_name: str, result: dict[str, Any], state: dict[str, Any], agent_label: str) -> str | None:
+    return f"[{agent_label}] Fatura consultada: {result}."
+
+
+def render_telecom_plan(*, tool_name: str, result: dict[str, Any], state: dict[str, Any], agent_label: str) -> str | None:
+    plano = result.get("plano")
+    if plano is None:
+        return None
+    parts = [f"[{agent_label}] Seu plano é {plano}"]
+    internet_gb = result.get("internet_gb")
+    status = result.get("status")
+    if internet_gb is not None:
+        parts.append(f"com {internet_gb} GB")
+    if status is not None:
+        parts.append(f"status {status}")
+    return ", ".join(parts) + "."
+
+
+def render_retail_order(*, tool_name: str, result: dict[str, Any], state: dict[str, Any], agent_label: str) -> str | None:
+    order_id = result.get("order_id")
+    status = result.get("status")
+    if order_id is None or status is None:
+        return None
+    lines = [f"[{agent_label}] Pedido {order_id}: status {status}."]
+    total = result.get("valor_total")
+    if total is not None:
+        lines.append(f"Valor total: R$ {_money_brl(total)}.")
+    items = result.get("itens") or []
+    rendered_items: list[str] = []
+    if isinstance(items, list):
+        for item in items:
+            if isinstance(item, dict):
+                value = item.get("descricao") or item.get("nome") or item.get("sku")
+            else:
+                value = item
+            if value not in (None, ""):
+                rendered_items.append(str(value))
+    if rendered_items:
+        lines.append("Itens: " + "; ".join(rendered_items) + ".")
+    return " ".join(lines)
+
+
+def render_retail_delivery(*, tool_name: str, result: dict[str, Any], state: dict[str, Any], agent_label: str) -> str | None:
+    order_id = result.get("order_id")
+    transportadora = result.get("transportadora")
+    codigo = result.get("codigo_rastreio")
+    previsao = result.get("previsao_entrega")
+    if any(v is None for v in (order_id, transportadora, codigo, previsao)):
+        return None
+    return (
+        f"[{agent_label}] Entrega do pedido {order_id}: transportadora {transportadora}, "
+        f"rastreio {codigo}, previsão {previsao}."
+    )
+
+
+def register_tool_renderers() -> None:
+    register_tool_response_renderer("telecom.invoice", render_telecom_invoice)
+    register_tool_response_renderer("telecom.plan", render_telecom_plan)
+    register_tool_response_renderer("retail.order", render_retail_order)
+    register_tool_response_renderer("retail.delivery", render_retail_delivery)

Tuning-Performance/Authentication/agent_template_backend_authentication/config/mcp_parameter_mapping.yaml

@@ -60,6 +60,18 @@ mcp_parameter_mapping:
             mensagem.
           pattern: (?i)\\b(?:pedido|order)\\s*[:#-]?\\s*([A-Z0-9-]+)\\b
           group: 1
+    cancelar_pedido:
+      map:
+        session_key: session_id
+      extract:
+        order_id:
+          from: message
+          type: string
+          strategy: hybrid
+          description: Extraia somente o identificador do pedido informado explicitamente pelo usuário. Retorne null quando não houver identificador de pedido na mensagem.
+          pattern: (?i)\\b(?:pedido|order)\\s*[:#-]?\\s*([A-Z0-9-]+)\\b
+          group: 1
+
     solicitar_troca:
       map:
         session_key: session_id

Tuning-Performance/Authentication/agent_template_backend_authentication/config/routing.yaml

@@ -79,6 +79,25 @@ intents:
       - Quero saber sobre meu pacote de internet.
       - Tenho roaming internacional?
 
+
+  - name: retail_order_cancel
+    domain: retail
+    agent: orders_agent
+    description: Cancelamento explícito de pedido ou compra.
+    priority: 20
+    mcp_tools:
+      - consultar_pedido
+      - cancelar_pedido
+    keywords:
+      - cancelar pedido
+      - cancelamento do pedido
+      - cancelar a compra
+      - cancelar compra
+    examples:
+      - Quero cancelar meu pedido.
+      - Cancele o pedido.
+      - Quero cancelar a compra.
+
   - name: retail_order_tracking
     domain: retail
     agent: orders_agent

Tuning-Performance/Authentication/agent_template_backend_authentication/config/tool_policies.yaml

@@ -7,6 +7,12 @@ defaults:
   require_confirmation: false
 
 tool_policies:
+  cancelar_pedido:
+    operation_type: transactional
+    require_confirmation: true
+    requires: [order_id]
+
+
   solicitar_troca:
     operation_type: transactional
     require_confirmation: true

Tuning-Performance/Authentication/agent_template_backend_authentication/config/tools.yaml

@@ -10,6 +10,9 @@ tools:
     - fatura
     - conta
     - boleto
+    response:
+      mode: renderer
+      renderer: telecom.invoice
   consultar_pagamentos:
     description: Consulta histórico de pagamentos do cliente.
     mcp_server: telecom
@@ -28,6 +31,9 @@ tools:
       asset_id: string
     selection_keywords:
     - plano
+    response:
+      mode: renderer
+      renderer: telecom.plan
   listar_servicos:
     description: Lista serviços ativos e adicionais VAS.
     mcp_server: telecom
@@ -49,6 +55,9 @@ tools:
     - consultar pedido
     - status do pedido
     - pedido
+    response:
+      mode: renderer
+      renderer: retail.order
   consultar_entrega:
     description: Consulta entrega e rastreamento do pedido.
     mcp_server: retail
@@ -61,6 +70,26 @@ tools:
     - rastreamento
     - transportadora
     - previsão
+    response:
+      mode: renderer
+      renderer: retail.delivery
+  cancelar_pedido:
+    description: Simula o cancelamento de um pedido de varejo.
+    mcp_server: retail
+    enabled: true
+    tool_type: action
+    requires:
+    - order_id
+    confirmation_required: true
+    args_schema:
+      order_id: string
+    selection_keywords:
+    - cancelar pedido
+    - cancelamento do pedido
+    - cancelar compra
+    - cancelar a compra
+
+
   solicitar_troca:
     description: Simula abertura de solicitação de troca.
     mcp_server: retail

Tuning-Performance/Deterministic_Transactional_Workflow/agent_template_backend/.env.example

@@ -0,0 +1,211 @@
+###############################################################################
+# AI AGENT PLATFORM - CONFIGURAÇÃO ÚNICA
+# Este arquivo é lido por Pydantic Settings no framework e no backend template.
+###############################################################################
+
+APP_NAME=ai-agent-template
+APP_ENV=local
+LOG_LEVEL=INFO
+API_HOST=0.0.0.0
+API_PORT=8000
+CORS_ORIGINS=http://localhost:5173,http://127.0.0.1:5173
+
+###############################################################################
+# LLM - OCI Generative AI como provider principal
+###############################################################################
+# Opções: mock, oci_openai, oci_sdk, openai_compatible
+LLM_PROVIDER=oci_openai
+LLM_TEMPERATURE=0.2
+LLM_MAX_TOKENS=2048
+LLM_TIMEOUT_SECONDS=120
+
+# OCI OpenAI-compatible endpoint
+OCI_GENAI_BASE_URL=https://inference.generativeai.us-chicago-1.oci.oraclecloud.com/openai/v1
+OCI_GENAI_MODEL=openai.gpt-4.1
+OCI_GENAI_API_KEY=sk-ph3FgX6ph3FgX6ph3FgX6ph3FgX6ph3FgX6ph3FgX6
+OCI_GENAI_PROJECT_OCID=
+
+# OCI_AUTH_MODE=config_file|instance_principal|resource_principal
+OCI_AUTH_MODE=config_file
+# OCI SDK / signer / profiles
+OCI_CONFIG_FILE=~/.oci/config
+OCI_PROFILE=DEFAULT
+OCI_COMPARTMENT_ID=ocid1.compartment.oc1..aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa
+OCI_REGION=us-chicago-1
+
+###############################################################################
+# Persistência
+###############################################################################
+# Opções: memory, autonomous, mongodb
+SESSION_REPOSITORY_PROVIDER=autonomous
+MEMORY_REPOSITORY_PROVIDER=autonomous
+CHECKPOINT_REPOSITORY_PROVIDER=autonomous
+
+# Autonomous Database
+ADB_USER=admin
+ADB_PASSWORD=fjhsdf04954hf
+ADB_DSN=oradb23aidev_high
+ADB_WALLET_LOCATION=/ORACLE/DEFAULT/Wallet_ORADB23aiDev
+ADB_WALLET_PASSWORD=fjhsdf04954hf
+ADB_TABLE_PREFIX=AGENTFW
+
+# MongoDB - também pode representar Autonomous usando API compatível com Mongo, se habilitada no ambiente
+MONGODB_URI=mongodb://mongo:mongopassword@localhost:27017
+MONGODB_DATABASE=agent_platform
+
+# Redis
+REDIS_URL=redis://localhost:6379/0
+ENABLE_REDIS_CACHE=false
+
+###############################################################################
+# RAG / Vector / Graph
+###############################################################################
+VECTOR_STORE_PROVIDER=memory
+GRAPH_STORE_PROVIDER=memory
+RAG_TOP_K=5
+EMBEDDING_PROVIDER=mock
+OCI_EMBEDDING_MODEL=cohere.embed-multilingual-v3.0
+RAG_FILE_GLOBS=*.md,*.txt,*.yaml,*.yml,*.json
+
+###############################################################################
+# Observabilidade
+###############################################################################
+ENABLE_LANGFUSE=true
+LANGFUSE_TRACE_MODE=verbose # Opcional: verbose, compact
+LANGFUSE_ROOT_SPAN_NAME=agent.gateway_message
+LANGFUSE_LEGACY_IO_FALLBACK=true
+LANGFUSE_PUBLIC_KEY=pk-lf-bd9b0c7e-2b8b-4e5b-a382-284a9b4413b3
+LANGFUSE_SECRET_KEY=sk-lf-5f5cc18d-0bb5-424e-b5d0-cb3664d58c20
+LANGFUSE_HOST=http://localhost:3005
+ENABLE_OTEL=false
+OTEL_EXPORTER_OTLP_ENDPOINT=
+OTEL_SERVICE_NAME=ai-agent-template
+ENABLE_LANGFUSE_OPENAI_AUTO_INSTRUMENTATION=true
+
+###############################################################################
+# Analytics / Observer corporativo
+###############################################################################
+# Quando true, AgentObserver publica eventos IC.*, NOC.* e GRL.* nos providers abaixo.
+ENABLE_ANALYTICS=false
+# Providers aceitos: oci_streaming,pubsub,noop
+ANALYTICS_PROVIDERS=pubsub
+# Compatibilidade FIRST/TIM: pode informar AGENT_PUBSUB_TOPIC diretamente.
+AGENT_PUBSUB_TOPIC=
+GCP_PUBSUB_TOPIC_PATH=
+GCP_PROJECT_ID=
+GCP_PUBSUB_TOPIC=
+GCP_PUBSUB_TIMEOUT_SECONDS=30
+# Credencial GCP segue padrão Google:
+# GOOGLE_APPLICATION_CREDENTIALS=/secrets/gcp-service-account.json
+
+###############################################################################
+# OCI Streaming
+###############################################################################
+ENABLE_OCI_STREAMING=false
+OCI_STREAM_ENDPOINT=
+OCI_STREAM_OCID=
+OCI_STREAM_PARTITION_KEY=agent-events
+
+###############################################################################
+# Guardrails, Judges, Supervisor
+###############################################################################
+ENABLE_INPUT_GUARDRAILS=true
+ENABLE_OUTPUT_GUARDRAILS=true
+ENABLE_JUDGES=true
+ENABLE_SUPERVISOR=true
+ENABLE_OUTPUT_SUPERVISOR=true
+ENABLE_PARALLEL_GUARDRAILS=true
+GUARDRAILS_FAIL_FAST=true
+OUTPUT_SUPERVISOR_MAX_RETRIES=3
+GUARDRAILS_CONFIG_PATH=./config/guardrails.yaml
+JUDGES_CONFIG_PATH=./config/judges.yaml
+PROMPT_POLICY_PATH=./config/prompt_policy.yaml
+
+###############################################################################
+# Gateway de canais
+###############################################################################
+DEFAULT_CHANNEL=web
+# embedded = backend may parse simple/native channel payloads.
+# external = backend only accepts GatewayRequest normalized by an external Channel Gateway.
+FRAMEWORK_CHANNEL_INPUT_MODE=embedded
+ENABLE_VOICE_ADAPTER=true
+ENABLE_WHATSAPP_ADAPTER=true
+ENABLE_TEXT_ADAPTER=true
+
+#################################################
+# ENTERPRISE ROUTING
+#################################################
+# Arquivo YAML com intents, keywords, políticas de estado e fallback.
+ROUTING_CONFIG_PATH=./config/routing.yaml
+# true = usa LLM para classificar quando keywords/estado não resolverem.
+# Em produção, costuma ser útil; em desenvolvimento, false evita custo e latência.
+ENABLE_LLM_ROUTER=true
+
+# Semantic route stickiness (optional).
+# Uses a lightweight LLM profile to decide only CONTINUE vs ROUTE.
+# There are no regexes or deterministic language rules.
+ENABLE_ROUTE_STICKINESS=true
+ROUTE_STICKINESS_LLM_PROFILE=route_continuity
+ROUTE_STICKINESS_CONFIDENCE_THRESHOLD=0.90
+ROUTE_STICKINESS_HISTORY_TURNS=2
+ROUTE_STICKINESS_MAX_TOKENS=80
+HUMAN_HANDOFF_MESSAGE=Vou encaminhar seu atendimento para uma pessoa.
+END_SESSION_MESSAGE=Atendimento encerrado. Obrigado pelo contato.
+ENABLE_TRANSACTIONAL_WORKFLOWS=true
+WORKFLOWS_PATH=./workflows
+
+###############################################################################
+# MCP / Tools
+###############################################################################
+ENABLE_MCP_TOOLS=true
+MCP_SERVERS_CONFIG_PATH=./config/mcp_servers.yaml
+TOOLS_CONFIG_PATH=./config/tools.yaml
+TOOL_POLICIES_PATH=./config/tool_policies.yaml
+MCP_TOOL_TIMEOUT_SECONDS=30
+
+# router = EnterpriseRouter seleciona um agente; supervisor = pode acionar múltiplos agentes
+ROUTING_MODE=router
+
+# Usage/cost accounting
+USAGE_REPOSITORY_PROVIDER=autonomous
+IDENTITY_CONFIG_PATH=./config/identity.yaml
+MCP_PARAMETER_MAPPING_PATH=./config/mcp_parameter_mapping.yaml
+
+# -----------------------------------------------------------------------------
+# ConversationSummaryMemory / compressão de contexto conversacional
+# -----------------------------------------------------------------------------
+ENABLE_CONVERSATION_SUMMARY_MEMORY=true
+MEMORY_CONTEXT_STRATEGY=summary
+MEMORY_HISTORY_LIMIT=80
+MEMORY_RECENT_MESSAGES_LIMIT=8
+MEMORY_SUMMARY_TRIGGER_MESSAGES=20
+MEMORY_MAX_SUMMARY_CHARS=6000
+MEMORY_SUMMARY_USE_LLM=true
+MEMORY_INJECT_RECENT_MESSAGES=true
+MEMORY_INJECT_SUMMARY=true
+
+###############################################################################
+# MCP Gateway
+###############################################################################
+# true = framework routes tool calls to the dedicated MCP Gateway.
+# false = framework calls MCP servers directly from mcp_servers.yaml.
+MCP_GATEWAY_ENABLED=true
+MCP_GATEWAY_URL=http://localhost:8300
+MCP_GATEWAY_TIMEOUT_SECONDS=60
+# MCP_GATEWAY_TOKEN=
+MCP_GATEWAY_AGENT_ID=telecom_contas
+MCP_GATEWAY_TENANT_ID=default
+
+###############################################################################
+# LONG-TERM MEMORY
+###############################################################################
+ENABLE_LONG_TERM_MEMORY=true
+LONG_TERM_MEMORY_PROVIDER=sqlite
+LONG_TERM_MEMORY_SQLITE_PATH=./data/agent_framework.db
+LONG_TERM_MEMORY_TABLE=agentfw_long_term_memory
+# For Autonomous/Oracle, defaults to ${ADB_TABLE_PREFIX}_LONG_TERM_MEMORY
+# LONG_TERM_MEMORY_ORACLE_TABLE=AGENTFW_LONG_TERM_MEMORY
+LONG_TERM_MEMORY_MAX_CONTEXT_ITEMS=20
+LONG_TERM_MEMORY_MIN_CONFIDENCE=0.70
+LONG_TERM_MEMORY_AUTO_EXTRACT=true
+LONG_TERM_MEMORY_INJECT_CONTEXT=true

Tuning-Performance/Deterministic_Transactional_Workflow/agent_template_backend/app/presentation/__init__.py

@@ -0,0 +1,3 @@
+from .tool_renderers import register_tool_renderers
+
+__all__ = ["register_tool_renderers"]

Tuning-Performance/Deterministic_Transactional_Workflow/agent_template_backend/app/presentation/tool_renderers.py

@@ -0,0 +1,74 @@
+from __future__ import annotations
+
+from typing import Any
+
+from agent_framework.presentation import register_tool_response_renderer
+
+
+def _money_brl(value: Any) -> str:
+    try:
+        return f"{float(value):.2f}".replace(".", ",")
+    except (TypeError, ValueError):
+        return str(value)
+
+
+def render_telecom_invoice(*, tool_name: str, result: dict[str, Any], state: dict[str, Any], agent_label: str) -> str | None:
+    return f"[{agent_label}] Fatura consultada: {result}."
+
+
+def render_telecom_plan(*, tool_name: str, result: dict[str, Any], state: dict[str, Any], agent_label: str) -> str | None:
+    plano = result.get("plano")
+    if plano is None:
+        return None
+    parts = [f"[{agent_label}] Seu plano é {plano}"]
+    internet_gb = result.get("internet_gb")
+    status = result.get("status")
+    if internet_gb is not None:
+        parts.append(f"com {internet_gb} GB")
+    if status is not None:
+        parts.append(f"status {status}")
+    return ", ".join(parts) + "."
+
+
+def render_retail_order(*, tool_name: str, result: dict[str, Any], state: dict[str, Any], agent_label: str) -> str | None:
+    order_id = result.get("order_id")
+    status = result.get("status")
+    if order_id is None or status is None:
+        return None
+    lines = [f"[{agent_label}] Pedido {order_id}: status {status}."]
+    total = result.get("valor_total")
+    if total is not None:
+        lines.append(f"Valor total: R$ {_money_brl(total)}.")
+    items = result.get("itens") or []
+    rendered_items: list[str] = []
+    if isinstance(items, list):
+        for item in items:
+            if isinstance(item, dict):
+                value = item.get("descricao") or item.get("nome") or item.get("sku")
+            else:
+                value = item
+            if value not in (None, ""):
+                rendered_items.append(str(value))
+    if rendered_items:
+        lines.append("Itens: " + "; ".join(rendered_items) + ".")
+    return " ".join(lines)
+
+
+def render_retail_delivery(*, tool_name: str, result: dict[str, Any], state: dict[str, Any], agent_label: str) -> str | None:
+    order_id = result.get("order_id")
+    transportadora = result.get("transportadora")
+    codigo = result.get("codigo_rastreio")
+    previsao = result.get("previsao_entrega")
+    if any(v is None for v in (order_id, transportadora, codigo, previsao)):
+        return None
+    return (
+        f"[{agent_label}] Entrega do pedido {order_id}: transportadora {transportadora}, "
+        f"rastreio {codigo}, previsão {previsao}."
+    )
+
+
+def register_tool_renderers() -> None:
+    register_tool_response_renderer("telecom.invoice", render_telecom_invoice)
+    register_tool_response_renderer("telecom.plan", render_telecom_plan)
+    register_tool_response_renderer("retail.order", render_retail_order)
+    register_tool_response_renderer("retail.delivery", render_retail_delivery)

Tuning-Performance/Deterministic_Transactional_Workflow/agent_template_backend/config/mcp_parameter_mapping.yaml

@@ -60,6 +60,18 @@ mcp_parameter_mapping:
             mensagem.
           pattern: (?i)\\b(?:pedido|order)\\s*[:#-]?\\s*([A-Z0-9-]+)\\b
           group: 1
+    cancelar_pedido:
+      map:
+        session_key: session_id
+      extract:
+        order_id:
+          from: message
+          type: string
+          strategy: hybrid
+          description: Extraia somente o identificador do pedido informado explicitamente pelo usuário. Retorne null quando não houver identificador de pedido na mensagem.
+          pattern: (?i)\\b(?:pedido|order)\\s*[:#-]?\\s*([A-Z0-9-]+)\\b
+          group: 1
+
     solicitar_troca:
       map:
         session_key: session_id

Tuning-Performance/Deterministic_Transactional_Workflow/agent_template_backend/config/routing.yaml

@@ -79,6 +79,25 @@ intents:
       - Quero saber sobre meu pacote de internet.
       - Tenho roaming internacional?
 
+
+  - name: retail_order_cancel
+    domain: retail
+    agent: orders_agent
+    description: Cancelamento explícito de pedido ou compra.
+    priority: 20
+    mcp_tools:
+      - consultar_pedido
+      - cancelar_pedido
+    keywords:
+      - cancelar pedido
+      - cancelamento do pedido
+      - cancelar a compra
+      - cancelar compra
+    examples:
+      - Quero cancelar meu pedido.
+      - Cancele o pedido.
+      - Quero cancelar a compra.
+
   - name: retail_order_tracking
     domain: retail
     agent: orders_agent

Tuning-Performance/Deterministic_Transactional_Workflow/agent_template_backend/config/tool_policies.yaml

@@ -7,6 +7,12 @@ defaults:
   require_confirmation: false
 
 tool_policies:
+  cancelar_pedido:
+    operation_type: transactional
+    require_confirmation: true
+    requires: [order_id]
+
+
   solicitar_troca:
     operation_type: transactional
     require_confirmation: true

Tuning-Performance/Deterministic_Transactional_Workflow/agent_template_backend/config/tools.yaml

@@ -10,6 +10,9 @@ tools:
     - fatura
     - conta
     - boleto
+    response:
+      mode: renderer
+      renderer: telecom.invoice
   consultar_pagamentos:
     description: Consulta histórico de pagamentos do cliente.
     mcp_server: telecom
@@ -28,6 +31,9 @@ tools:
       asset_id: string
     selection_keywords:
     - plano
+    response:
+      mode: renderer
+      renderer: telecom.plan
   listar_servicos:
     description: Lista serviços ativos e adicionais VAS.
     mcp_server: telecom
@@ -49,6 +55,9 @@ tools:
     - consultar pedido
     - status do pedido
     - pedido
+    response:
+      mode: renderer
+      renderer: retail.order
   consultar_entrega:
     description: Consulta entrega e rastreamento do pedido.
     mcp_server: retail
@@ -61,6 +70,26 @@ tools:
     - rastreamento
     - transportadora
     - previsão
+    response:
+      mode: renderer
+      renderer: retail.delivery
+  cancelar_pedido:
+    description: Simula o cancelamento de um pedido de varejo.
+    mcp_server: retail
+    enabled: true
+    tool_type: action
+    requires:
+    - order_id
+    confirmation_required: true
+    args_schema:
+      order_id: string
+    selection_keywords:
+    - cancelar pedido
+    - cancelamento do pedido
+    - cancelar compra
+    - cancelar a compra
+
+
   solicitar_troca:
     description: Simula abertura de solicitação de troca.
     mcp_server: retail

Tuning-Performance/Domain_Requested_LLM_Composition/README.md

@@ -0,0 +1,31 @@
+# Domain Requested LLM Composition
+
+## Objetivo
+
+Permitir que uma tool/workflow de domínio informe que o resultado operacional não deve ser devolvido diretamente ao usuário e precisa ser redigido pelo LLM oficial do agente, sem criar um gateway LLM dentro do domínio.
+
+## Contrato
+
+A tool pode devolver, em qualquer nível do resultado:
+
+```json
+{
+  "requires_llm_composition": true,
+  "response_instruction": "Explique ao cliente a forma de devolução usando apenas os dados do workflow."
+}
+```
+
+Também é aceito `response_instructions` como lista.
+
+O `AgentRuntimeMixin` percorre recursivamente o resultado MCP. Quando a flag está ativa, `build_direct_mcp_answer()` retorna `None`; a resposta segue pelo LLM configurado no framework e recebe as evidências MCP no contexto normal do agente.
+
+## Por que isso existe
+
+Algumas operações, como pró-rata, possuem resultado determinístico e efeitos já concluídos, mas precisam de linguagem natural adequada ao canal. Antes, o domínio Contas possuía um gateway LLM próprio. Agora o domínio só declara a necessidade e a instrução; execução, credenciais, profiles, tracing e custos continuam no `agent_framework_oci`.
+
+## Regras
+
+- Não usar para decidir se uma transação deve ocorrer.
+- Não usar para inventar valores ou protocolos.
+- A instrução deve exigir que o LLM use somente as evidências retornadas pela tool/workflow.
+- Pode coexistir com `requires_rag`; nesse caso o framework também executa RAG antes da composição.

Tuning-Performance/Domain_Requested_RAG/README.md

@@ -0,0 +1,51 @@
+# Domain-Requested RAG
+
+## Objetivo
+
+Permitir que uma tool/workflow de domínio declare que o resultado MCP **não é suficiente** para produzir a resposta final e que o agente deve recuperar conhecimento usando o `RagService` oficial do `agent_framework_oci`.
+
+O domínio não instancia banco vetorial, embeddings, LLM ou cliente RAG. Ele apenas devolve no resultado:
+
+```json
+{
+  "requires_rag": true,
+  "rag_queries": [
+    "Como cancelar o serviço Paramount+ no parceiro? Procedimento oficial de cancelamento."
+  ]
+}
+```
+
+## Fluxo
+
+```text
+Workflow/tool de domínio
+        |
+        | requires_rag + rag_queries
+        v
+AgentRuntimeMixin
+        |
+        +-- impede direct MCP answer
+        +-- ignora SKIP_RAG_WHEN_MCP_SUFFICIENT para este resultado
+        +-- usa rag_query/rag_queries como query override
+        v
+RagService
+        v
+Vector/Graph store configurado no framework
+        v
+LLM do agente com MCP evidence + RAG evidence
+```
+
+## Regras
+
+1. `requires_rag=false` ou ausente mantém o comportamento padrão.
+2. `SKIP_RAG_WHEN_MCP_SUFFICIENT=true` continua válido para tools normais.
+3. Quando `requires_rag=true`, o framework não usa `build_direct_mcp_answer()`.
+4. `rag_query` aceita uma query; `rag_queries` aceita várias queries, preservadas e deduplicadas em ordem.
+5. O domínio nunca executa `RagService` diretamente.
+6. Guardrails de retrieval (`RAGSEC`, `RET_REL` etc.) continuam executando normalmente sobre o contexto recuperado.
+
+## Exemplo Contas
+
+No fluxo VAS Estratégico, após o cliente rejeitar a explicação e pedir cancelamento, a action devolve queries específicas por parceiro. O `VasAgent` usa o `RagService` do framework para recuperar o procedimento oficial e compor a resposta.
+
+Isso substitui o comportamento legado em que o backend Contas possuía uma busca RAG própria dentro de `vas_strategic()`.

Tuning-Performance/Long_Term_Memory/templates/agent_template_backend/app/agents/runtime.py

@@ -3,5 +3,8 @@ from __future__ import annotations
 # Compatibilidade local do template/backend.
 # A implementação oficial agora fica no framework para evitar duplicação entre agentes.
 from agent_framework.runtime import AgentRuntimeMixin, MessageBuilder, RuntimeContext
+from app.presentation import register_tool_renderers
+
+register_tool_renderers()
 
 __all__ = ["AgentRuntimeMixin", "MessageBuilder", "RuntimeContext"]

Tuning-Performance/Long_Term_Memory/templates/agent_template_backend/app/presentation/__init__.py

@@ -0,0 +1,3 @@
+from .tool_renderers import register_tool_renderers
+
+__all__ = ["register_tool_renderers"]

Tuning-Performance/Long_Term_Memory/templates/agent_template_backend/app/presentation/tool_renderers.py

@@ -0,0 +1,74 @@
+from __future__ import annotations
+
+from typing import Any
+
+from agent_framework.presentation import register_tool_response_renderer
+
+
+def _money_brl(value: Any) -> str:
+    try:
+        return f"{float(value):.2f}".replace(".", ",")
+    except (TypeError, ValueError):
+        return str(value)
+
+
+def render_telecom_invoice(*, tool_name: str, result: dict[str, Any], state: dict[str, Any], agent_label: str) -> str | None:
+    return f"[{agent_label}] Fatura consultada: {result}."
+
+
+def render_telecom_plan(*, tool_name: str, result: dict[str, Any], state: dict[str, Any], agent_label: str) -> str | None:
+    plano = result.get("plano")
+    if plano is None:
+        return None
+    parts = [f"[{agent_label}] Seu plano é {plano}"]
+    internet_gb = result.get("internet_gb")
+    status = result.get("status")
+    if internet_gb is not None:
+        parts.append(f"com {internet_gb} GB")
+    if status is not None:
+        parts.append(f"status {status}")
+    return ", ".join(parts) + "."
+
+
+def render_retail_order(*, tool_name: str, result: dict[str, Any], state: dict[str, Any], agent_label: str) -> str | None:
+    order_id = result.get("order_id")
+    status = result.get("status")
+    if order_id is None or status is None:
+        return None
+    lines = [f"[{agent_label}] Pedido {order_id}: status {status}."]
+    total = result.get("valor_total")
+    if total is not None:
+        lines.append(f"Valor total: R$ {_money_brl(total)}.")
+    items = result.get("itens") or []
+    rendered_items: list[str] = []
+    if isinstance(items, list):
+        for item in items:
+            if isinstance(item, dict):
+                value = item.get("descricao") or item.get("nome") or item.get("sku")
+            else:
+                value = item
+            if value not in (None, ""):
+                rendered_items.append(str(value))
+    if rendered_items:
+        lines.append("Itens: " + "; ".join(rendered_items) + ".")
+    return " ".join(lines)
+
+
+def render_retail_delivery(*, tool_name: str, result: dict[str, Any], state: dict[str, Any], agent_label: str) -> str | None:
+    order_id = result.get("order_id")
+    transportadora = result.get("transportadora")
+    codigo = result.get("codigo_rastreio")
+    previsao = result.get("previsao_entrega")
+    if any(v is None for v in (order_id, transportadora, codigo, previsao)):
+        return None
+    return (
+        f"[{agent_label}] Entrega do pedido {order_id}: transportadora {transportadora}, "
+        f"rastreio {codigo}, previsão {previsao}."
+    )
+
+
+def register_tool_renderers() -> None:
+    register_tool_response_renderer("telecom.invoice", render_telecom_invoice)
+    register_tool_response_renderer("telecom.plan", render_telecom_plan)
+    register_tool_response_renderer("retail.order", render_retail_order)
+    register_tool_response_renderer("retail.delivery", render_retail_delivery)

Tuning-Performance/Long_Term_Memory/templates/agent_template_backend/config/mcp_parameter_mapping.yaml

@@ -60,6 +60,18 @@ mcp_parameter_mapping:
             mensagem.
           pattern: (?i)\\b(?:pedido|order)\\s*[:#-]?\\s*([A-Z0-9-]+)\\b
           group: 1
+    cancelar_pedido:
+      map:
+        session_key: session_id
+      extract:
+        order_id:
+          from: message
+          type: string
+          strategy: hybrid
+          description: Extraia somente o identificador do pedido informado explicitamente pelo usuário. Retorne null quando não houver identificador de pedido na mensagem.
+          pattern: (?i)\\b(?:pedido|order)\\s*[:#-]?\\s*([A-Z0-9-]+)\\b
+          group: 1
+
     solicitar_troca:
       map:
         session_key: session_id

Tuning-Performance/Long_Term_Memory/templates/agent_template_backend/config/routing.yaml

@@ -79,6 +79,25 @@ intents:
       - Quero saber sobre meu pacote de internet.
       - Tenho roaming internacional?
 
+
+  - name: retail_order_cancel
+    domain: retail
+    agent: orders_agent
+    description: Cancelamento explícito de pedido ou compra.
+    priority: 20
+    mcp_tools:
+      - consultar_pedido
+      - cancelar_pedido
+    keywords:
+      - cancelar pedido
+      - cancelamento do pedido
+      - cancelar a compra
+      - cancelar compra
+    examples:
+      - Quero cancelar meu pedido.
+      - Cancele o pedido.
+      - Quero cancelar a compra.
+
   - name: retail_order_tracking
     domain: retail
     agent: orders_agent

Tuning-Performance/Long_Term_Memory/templates/agent_template_backend/config/tool_policies.yaml

@@ -7,6 +7,12 @@ defaults:
   require_confirmation: false
 
 tool_policies:
+  cancelar_pedido:
+    operation_type: transactional
+    require_confirmation: true
+    requires: [order_id]
+
+
   solicitar_troca:
     operation_type: transactional
     require_confirmation: true

Tuning-Performance/Long_Term_Memory/templates/agent_template_backend/config/tools.yaml

@@ -10,6 +10,9 @@ tools:
     - fatura
     - conta
     - boleto
+    response:
+      mode: renderer
+      renderer: telecom.invoice
   consultar_pagamentos:
     description: Consulta histórico de pagamentos do cliente.
     mcp_server: telecom
@@ -28,6 +31,9 @@ tools:
       asset_id: string
     selection_keywords:
     - plano
+    response:
+      mode: renderer
+      renderer: telecom.plan
   listar_servicos:
     description: Lista serviços ativos e adicionais VAS.
     mcp_server: telecom
@@ -49,6 +55,9 @@ tools:
     - consultar pedido
     - status do pedido
     - pedido
+    response:
+      mode: renderer
+      renderer: retail.order
   consultar_entrega:
     description: Consulta entrega e rastreamento do pedido.
     mcp_server: retail
@@ -61,6 +70,26 @@ tools:
     - rastreamento
     - transportadora
     - previsão
+    response:
+      mode: renderer
+      renderer: retail.delivery
+  cancelar_pedido:
+    description: Simula o cancelamento de um pedido de varejo.
+    mcp_server: retail
+    enabled: true
+    tool_type: action
+    requires:
+    - order_id
+    confirmation_required: true
+    args_schema:
+      order_id: string
+    selection_keywords:
+    - cancelar pedido
+    - cancelamento do pedido
+    - cancelar compra
+    - cancelar a compra
+
+
   solicitar_troca:
     description: Simula abertura de solicitação de troca.
     mcp_server: retail

Tuning-Performance/Normal/templates/agent_template_backend/.env.example

@@ -0,0 +1,195 @@
+###############################################################################
+# AI AGENT PLATFORM - CONFIGURAÇÃO ÚNICA
+# Este arquivo é lido por Pydantic Settings no framework e no backend template.
+###############################################################################
+
+APP_NAME=ai-agent-template
+APP_ENV=local
+LOG_LEVEL=INFO
+API_HOST=0.0.0.0
+API_PORT=8000
+CORS_ORIGINS=http://localhost:5173,http://127.0.0.1:5173
+
+###############################################################################
+# LLM - OCI Generative AI como provider principal
+###############################################################################
+# Opções: mock, oci_openai, oci_sdk, openai_compatible
+LLM_PROVIDER=oci_openai
+LLM_TEMPERATURE=0.2
+LLM_MAX_TOKENS=2048
+LLM_TIMEOUT_SECONDS=120
+
+# OCI OpenAI-compatible endpoint
+OCI_GENAI_BASE_URL=https://inference.generativeai.us-chicago-1.oci.oraclecloud.com/openai/v1
+OCI_GENAI_MODEL=openai.gpt-4.1
+OCI_GENAI_API_KEY=sk-ph3FgX6ph3FgX6ph3FgX6ph3FgX6ph3FgX6ph3FgX6
+OCI_GENAI_PROJECT_OCID=
+
+# OCI SDK / signer / profiles
+OCI_CONFIG_FILE=~/.oci/config
+OCI_PROFILE=DEFAULT
+OCI_COMPARTMENT_ID=ocid1.compartment.oc1..aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa
+OCI_REGION=us-chicago-1
+
+###############################################################################
+# Persistência
+###############################################################################
+# Opções: memory, autonomous, mongodb
+SESSION_REPOSITORY_PROVIDER=sqlite
+MEMORY_REPOSITORY_PROVIDER=sqlite
+CHECKPOINT_REPOSITORY_PROVIDER=sqlite
+SQLITE_DB_PATH=./data/agent_framework.db
+
+# Autonomous Database
+ADB_USER=admin
+ADB_PASSWORD=fjhsdf04954hf
+ADB_DSN=oradb23aidev_high
+ADB_WALLET_LOCATION=/ORACLE/DEFAULT/Wallet_ORADB23aiDev
+ADB_WALLET_PASSWORD=fjhsdf04954hf
+ADB_TABLE_PREFIX=AGENTFW
+
+# MongoDB - também pode representar Autonomous usando API compatível com Mongo, se habilitada no ambiente
+MONGODB_URI=mongodb://mongo:mongopassword@localhost:27017
+MONGODB_DATABASE=agent_platform
+
+# Redis
+REDIS_URL=redis://localhost:6379/0
+ENABLE_REDIS_CACHE=false
+
+###############################################################################
+# RAG / Vector / Graph
+###############################################################################
+VECTOR_STORE_PROVIDER=sqlite
+GRAPH_STORE_PROVIDER=sqlite
+RAG_TOP_K=5
+EMBEDDING_PROVIDER=mock
+OCI_EMBEDDING_MODEL=cohere.embed-multilingual-v3.0
+RAG_FILE_GLOBS=*.md,*.txt,*.yaml,*.yml,*.json
+
+###############################################################################
+# Observabilidade
+###############################################################################
+ENABLE_LANGFUSE=true
+LANGFUSE_TRACE_MODE=compact # Opcional: verbose, compact
+LANGFUSE_PUBLIC_KEY=pk-lf-2f9da109-5b0f-4c78-b61d-9598ed787eba
+LANGFUSE_SECRET_KEY=sk-lf-a4cb0cdd-f2ea-4468-9911-cebeb91ba944
+LANGFUSE_HOST=http://localhost:3005
+ENABLE_OTEL=false
+OTEL_EXPORTER_OTLP_ENDPOINT=
+OTEL_SERVICE_NAME=ai-agent-template
+ENABLE_LANGFUSE_OPENAI_AUTO_INSTRUMENTATION=true
+
+###############################################################################
+# Analytics / Observer corporativo
+###############################################################################
+# Quando true, AgentObserver publica eventos IC.*, NOC.* e GRL.* nos providers abaixo.
+ENABLE_ANALYTICS=false
+# Providers aceitos: oci_streaming,pubsub,noop
+ANALYTICS_PROVIDERS=pubsub
+# Compatibilidade FIRST/TIM: pode informar AGENT_PUBSUB_TOPIC diretamente.
+AGENT_PUBSUB_TOPIC=
+GCP_PUBSUB_TOPIC_PATH=
+GCP_PROJECT_ID=
+GCP_PUBSUB_TOPIC=
+GCP_PUBSUB_TIMEOUT_SECONDS=30
+# Credencial GCP segue padrão Google:
+# GOOGLE_APPLICATION_CREDENTIALS=/secrets/gcp-service-account.json
+
+###############################################################################
+# OCI Streaming
+###############################################################################
+ENABLE_OCI_STREAMING=false
+OCI_STREAM_ENDPOINT=
+OCI_STREAM_OCID=
+OCI_STREAM_PARTITION_KEY=agent-events
+
+###############################################################################
+# Guardrails, Judges, Supervisor
+###############################################################################
+ENABLE_INPUT_GUARDRAILS=true
+ENABLE_OUTPUT_GUARDRAILS=true
+ENABLE_JUDGES=true
+ENABLE_SUPERVISOR=true
+ENABLE_OUTPUT_SUPERVISOR=true
+ENABLE_PARALLEL_GUARDRAILS=true
+GUARDRAILS_FAIL_FAST=true
+OUTPUT_SUPERVISOR_MAX_RETRIES=3
+GUARDRAILS_CONFIG_PATH=./config/guardrails.yaml
+JUDGES_CONFIG_PATH=./config/judges.yaml
+PROMPT_POLICY_PATH=./config/prompt_policy.yaml
+
+###############################################################################
+# Gateway de canais
+###############################################################################
+DEFAULT_CHANNEL=web
+# embedded = backend may parse simple/native channel payloads.
+# external = backend only accepts GatewayRequest normalized by an external Channel Gateway.
+FRAMEWORK_CHANNEL_INPUT_MODE=embedded
+ENABLE_VOICE_ADAPTER=true
+ENABLE_WHATSAPP_ADAPTER=true
+ENABLE_TEXT_ADAPTER=true
+
+#################################################
+# ENTERPRISE ROUTING
+#################################################
+# Arquivo YAML com intents, keywords, políticas de estado e fallback.
+ROUTING_CONFIG_PATH=./config/routing.yaml
+# true = usa LLM para classificar quando keywords/estado não resolverem.
+# Em produção, costuma ser útil; em desenvolvimento, false evita custo e latência.
+ENABLE_LLM_ROUTER=true
+
+###############################################################################
+# MCP / Tools
+###############################################################################
+ENABLE_MCP_TOOLS=true
+MCP_SERVERS_CONFIG_PATH=./config/mcp_servers.yaml
+TOOLS_CONFIG_PATH=./config/tools.yaml
+TOOL_POLICIES_PATH=./config/tool_policies.yaml
+MCP_TOOL_TIMEOUT_SECONDS=30
+
+# router = EnterpriseRouter seleciona um agente; supervisor = pode acionar múltiplos agentes
+ROUTING_MODE=router
+
+# Usage/cost accounting
+USAGE_REPOSITORY_PROVIDER=sqlite
+IDENTITY_CONFIG_PATH=./config/identity.yaml
+MCP_PARAMETER_MAPPING_PATH=./config/mcp_parameter_mapping.yaml
+
+# -----------------------------------------------------------------------------
+# ConversationSummaryMemory / compressão de contexto conversacional
+# -----------------------------------------------------------------------------
+ENABLE_CONVERSATION_SUMMARY_MEMORY=true
+MEMORY_CONTEXT_STRATEGY=summary
+MEMORY_HISTORY_LIMIT=80
+MEMORY_RECENT_MESSAGES_LIMIT=8
+MEMORY_SUMMARY_TRIGGER_MESSAGES=20
+MEMORY_MAX_SUMMARY_CHARS=6000
+MEMORY_SUMMARY_USE_LLM=true
+MEMORY_INJECT_RECENT_MESSAGES=true
+MEMORY_INJECT_SUMMARY=true
+
+###############################################################################
+# MCP Gateway
+###############################################################################
+# true = framework routes tool calls to the dedicated MCP Gateway.
+# false = framework calls MCP servers directly from mcp_servers.yaml.
+MCP_GATEWAY_ENABLED=true
+MCP_GATEWAY_URL=http://localhost:8300
+MCP_GATEWAY_TIMEOUT_SECONDS=60
+# MCP_GATEWAY_TOKEN=
+MCP_GATEWAY_AGENT_ID=telecom_contas
+MCP_GATEWAY_TENANT_ID=default
+
+###############################################################################
+# LONG-TERM MEMORY
+###############################################################################
+ENABLE_LONG_TERM_MEMORY=true
+LONG_TERM_MEMORY_PROVIDER=sqlite
+LONG_TERM_MEMORY_SQLITE_PATH=./data/agent_framework.db
+LONG_TERM_MEMORY_TABLE=agentfw_long_term_memory
+# For Autonomous/Oracle, defaults to ${ADB_TABLE_PREFIX}_LONG_TERM_MEMORY
+# LONG_TERM_MEMORY_ORACLE_TABLE=AGENTFW_LONG_TERM_MEMORY
+LONG_TERM_MEMORY_MAX_CONTEXT_ITEMS=20
+LONG_TERM_MEMORY_MIN_CONFIDENCE=0.70
+LONG_TERM_MEMORY_AUTO_EXTRACT=true
+LONG_TERM_MEMORY_INJECT_CONTEXT=true

Tuning-Performance/Normal/templates/agent_template_backend/app/agents/runtime.py

@@ -3,5 +3,8 @@ from __future__ import annotations
 # Compatibilidade local do template/backend.
 # A implementação oficial agora fica no framework para evitar duplicação entre agentes.
 from agent_framework.runtime import AgentRuntimeMixin, MessageBuilder, RuntimeContext
+from app.presentation import register_tool_renderers
+
+register_tool_renderers()
 
 __all__ = ["AgentRuntimeMixin", "MessageBuilder", "RuntimeContext"]

Tuning-Performance/Normal/templates/agent_template_backend/app/presentation/__init__.py

@@ -0,0 +1,3 @@
+from .tool_renderers import register_tool_renderers
+
+__all__ = ["register_tool_renderers"]

Tuning-Performance/Normal/templates/agent_template_backend/app/presentation/tool_renderers.py

@@ -0,0 +1,74 @@
+from __future__ import annotations
+
+from typing import Any
+
+from agent_framework.presentation import register_tool_response_renderer
+
+
+def _money_brl(value: Any) -> str:
+    try:
+        return f"{float(value):.2f}".replace(".", ",")
+    except (TypeError, ValueError):
+        return str(value)
+
+
+def render_telecom_invoice(*, tool_name: str, result: dict[str, Any], state: dict[str, Any], agent_label: str) -> str | None:
+    return f"[{agent_label}] Fatura consultada: {result}."
+
+
+def render_telecom_plan(*, tool_name: str, result: dict[str, Any], state: dict[str, Any], agent_label: str) -> str | None:
+    plano = result.get("plano")
+    if plano is None:
+        return None
+    parts = [f"[{agent_label}] Seu plano é {plano}"]
+    internet_gb = result.get("internet_gb")
+    status = result.get("status")
+    if internet_gb is not None:
+        parts.append(f"com {internet_gb} GB")
+    if status is not None:
+        parts.append(f"status {status}")
+    return ", ".join(parts) + "."
+
+
+def render_retail_order(*, tool_name: str, result: dict[str, Any], state: dict[str, Any], agent_label: str) -> str | None:
+    order_id = result.get("order_id")
+    status = result.get("status")
+    if order_id is None or status is None:
+        return None
+    lines = [f"[{agent_label}] Pedido {order_id}: status {status}."]
+    total = result.get("valor_total")
+    if total is not None:
+        lines.append(f"Valor total: R$ {_money_brl(total)}.")
+    items = result.get("itens") or []
+    rendered_items: list[str] = []
+    if isinstance(items, list):
+        for item in items:
+            if isinstance(item, dict):
+                value = item.get("descricao") or item.get("nome") or item.get("sku")
+            else:
+                value = item
+            if value not in (None, ""):
+                rendered_items.append(str(value))
+    if rendered_items:
+        lines.append("Itens: " + "; ".join(rendered_items) + ".")
+    return " ".join(lines)
+
+
+def render_retail_delivery(*, tool_name: str, result: dict[str, Any], state: dict[str, Any], agent_label: str) -> str | None:
+    order_id = result.get("order_id")
+    transportadora = result.get("transportadora")
+    codigo = result.get("codigo_rastreio")
+    previsao = result.get("previsao_entrega")
+    if any(v is None for v in (order_id, transportadora, codigo, previsao)):
+        return None
+    return (
+        f"[{agent_label}] Entrega do pedido {order_id}: transportadora {transportadora}, "
+        f"rastreio {codigo}, previsão {previsao}."
+    )
+
+
+def register_tool_renderers() -> None:
+    register_tool_response_renderer("telecom.invoice", render_telecom_invoice)
+    register_tool_response_renderer("telecom.plan", render_telecom_plan)
+    register_tool_response_renderer("retail.order", render_retail_order)
+    register_tool_response_renderer("retail.delivery", render_retail_delivery)

Tuning-Performance/Normal/templates/agent_template_backend/config/mcp_parameter_mapping.yaml

@@ -60,6 +60,18 @@ mcp_parameter_mapping:
             mensagem.
           pattern: (?i)\\b(?:pedido|order)\\s*[:#-]?\\s*([A-Z0-9-]+)\\b
           group: 1
+    cancelar_pedido:
+      map:
+        session_key: session_id
+      extract:
+        order_id:
+          from: message
+          type: string
+          strategy: hybrid
+          description: Extraia somente o identificador do pedido informado explicitamente pelo usuário. Retorne null quando não houver identificador de pedido na mensagem.
+          pattern: (?i)\\b(?:pedido|order)\\s*[:#-]?\\s*([A-Z0-9-]+)\\b
+          group: 1
+
     solicitar_troca:
       map:
         session_key: session_id

Tuning-Performance/Normal/templates/agent_template_backend/config/routing.yaml

@@ -79,6 +79,25 @@ intents:
       - Quero saber sobre meu pacote de internet.
       - Tenho roaming internacional?
 
+
+  - name: retail_order_cancel
+    domain: retail
+    agent: orders_agent
+    description: Cancelamento explícito de pedido ou compra.
+    priority: 20
+    mcp_tools:
+      - consultar_pedido
+      - cancelar_pedido
+    keywords:
+      - cancelar pedido
+      - cancelamento do pedido
+      - cancelar a compra
+      - cancelar compra
+    examples:
+      - Quero cancelar meu pedido.
+      - Cancele o pedido.
+      - Quero cancelar a compra.
+
   - name: retail_order_tracking
     domain: retail
     agent: orders_agent

Tuning-Performance/Normal/templates/agent_template_backend/config/tool_policies.yaml

@@ -7,6 +7,12 @@ defaults:
   require_confirmation: false
 
 tool_policies:
+  cancelar_pedido:
+    operation_type: transactional
+    require_confirmation: true
+    requires: [order_id]
+
+
   solicitar_troca:
     operation_type: transactional
     require_confirmation: true

Tuning-Performance/Normal/templates/agent_template_backend/config/tools.yaml

@@ -10,6 +10,9 @@ tools:
     - fatura
     - conta
     - boleto
+    response:
+      mode: renderer
+      renderer: telecom.invoice
   consultar_pagamentos:
     description: Consulta histórico de pagamentos do cliente.
     mcp_server: telecom
@@ -28,6 +31,9 @@ tools:
       asset_id: string
     selection_keywords:
     - plano
+    response:
+      mode: renderer
+      renderer: telecom.plan
   listar_servicos:
     description: Lista serviços ativos e adicionais VAS.
     mcp_server: telecom
@@ -49,6 +55,9 @@ tools:
     - consultar pedido
     - status do pedido
     - pedido
+    response:
+      mode: renderer
+      renderer: retail.order
   consultar_entrega:
     description: Consulta entrega e rastreamento do pedido.
     mcp_server: retail
@@ -61,6 +70,26 @@ tools:
     - rastreamento
     - transportadora
     - previsão
+    response:
+      mode: renderer
+      renderer: retail.delivery
+  cancelar_pedido:
+    description: Simula o cancelamento de um pedido de varejo.
+    mcp_server: retail
+    enabled: true
+    tool_type: action
+    requires:
+    - order_id
+    confirmation_required: true
+    args_schema:
+      order_id: string
+    selection_keywords:
+    - cancelar pedido
+    - cancelamento do pedido
+    - cancelar compra
+    - cancelar a compra
+
+
   solicitar_troca:
     description: Simula abertura de solicitação de troca.
     mcp_server: retail

Tuning-Performance/Normal/templates/agent_template_backend_day_zero/.env.example

@@ -0,0 +1,192 @@
+###############################################################################
+# AI AGENT PLATFORM - CONFIGURAÇÃO ÚNICA
+# Este arquivo é lido por Pydantic Settings no framework e no backend template.
+###############################################################################
+
+APP_NAME=ai-agent-template
+APP_ENV=local
+LOG_LEVEL=INFO
+API_HOST=0.0.0.0
+API_PORT=8000
+CORS_ORIGINS=http://localhost:5173,http://127.0.0.1:5173
+
+###############################################################################
+# LLM - OCI Generative AI como provider principal
+###############################################################################
+# Opções: mock, oci_openai, oci_sdk, openai_compatible
+LLM_PROVIDER=oci_openai
+LLM_TEMPERATURE=0.2
+LLM_MAX_TOKENS=2048
+LLM_TIMEOUT_SECONDS=120
+
+# OCI OpenAI-compatible endpoint
+OCI_GENAI_BASE_URL=https://inference.generativeai.us-chicago-1.oci.oraclecloud.com/openai/v1
+OCI_GENAI_MODEL=openai.gpt-4.1
+OCI_GENAI_API_KEY=sk-ph3FgX6ph3FgX6ph3FgX6ph3FgX6ph3FgX6ph3FgX6
+OCI_GENAI_PROJECT_OCID=
+
+# OCI SDK / signer / profiles
+OCI_CONFIG_FILE=~/.oci/config
+OCI_PROFILE=DEFAULT
+OCI_COMPARTMENT_ID=ocid1.compartment.oc1..aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa
+OCI_REGION=us-chicago-1
+
+###############################################################################
+# Persistência
+###############################################################################
+# Opções: memory, autonomous, mongodb
+SESSION_REPOSITORY_PROVIDER=sqlite
+MEMORY_REPOSITORY_PROVIDER=sqlite
+CHECKPOINT_REPOSITORY_PROVIDER=sqlite
+SQLITE_DB_PATH=./data/agent_framework.db
+
+# Autonomous Database
+ADB_USER=admin
+ADB_PASSWORD=fjhsdf04954hf
+ADB_DSN=oradb23aidev_high
+ADB_WALLET_LOCATION=/ORACLE/DEFAULT/Wallet_ORADB23aiDev
+ADB_WALLET_PASSWORD=fjhsdf04954hf
+ADB_TABLE_PREFIX=AGENTFW
+
+# MongoDB - também pode representar Autonomous usando API compatível com Mongo, se habilitada no ambiente
+MONGODB_URI=mongodb://mongo:mongopassword@localhost:27017
+MONGODB_DATABASE=agent_platform
+
+# Redis
+REDIS_URL=redis://localhost:6379/0
+ENABLE_REDIS_CACHE=false
+
+###############################################################################
+# RAG / Vector / Graph
+###############################################################################
+VECTOR_STORE_PROVIDER=sqlite
+GRAPH_STORE_PROVIDER=sqlite
+RAG_TOP_K=5
+EMBEDDING_PROVIDER=mock
+OCI_EMBEDDING_MODEL=cohere.embed-multilingual-v3.0
+RAG_FILE_GLOBS=*.md,*.txt,*.yaml,*.yml,*.json
+
+###############################################################################
+# Observabilidade
+###############################################################################
+ENABLE_LANGFUSE=true
+LANGFUSE_TRACE_MODE=compact # Opcional: verbose, compact
+LANGFUSE_PUBLIC_KEY=pk-lf-2f9da109-5b0f-4c78-b61d-9598ed787eba
+LANGFUSE_SECRET_KEY=sk-lf-a4cb0cdd-f2ea-4468-9911-cebeb91ba944
+LANGFUSE_HOST=http://localhost:3005
+ENABLE_OTEL=false
+OTEL_EXPORTER_OTLP_ENDPOINT=
+OTEL_SERVICE_NAME=ai-agent-template
+ENABLE_LANGFUSE_OPENAI_AUTO_INSTRUMENTATION=true
+
+###############################################################################
+# Analytics / Observer corporativo
+###############################################################################
+# Quando true, AgentObserver publica eventos IC.*, NOC.* e GRL.* nos providers abaixo.
+ENABLE_ANALYTICS=false
+# Providers aceitos: oci_streaming,pubsub,noop
+ANALYTICS_PROVIDERS=pubsub
+# Compatibilidade FIRST/TIM: pode informar AGENT_PUBSUB_TOPIC diretamente.
+AGENT_PUBSUB_TOPIC=
+GCP_PUBSUB_TOPIC_PATH=
+GCP_PROJECT_ID=
+GCP_PUBSUB_TOPIC=
+GCP_PUBSUB_TIMEOUT_SECONDS=30
+# Credencial GCP segue padrão Google:
+# GOOGLE_APPLICATION_CREDENTIALS=/secrets/gcp-service-account.json
+
+###############################################################################
+# OCI Streaming
+###############################################################################
+ENABLE_OCI_STREAMING=false
+OCI_STREAM_ENDPOINT=
+OCI_STREAM_OCID=
+OCI_STREAM_PARTITION_KEY=agent-events
+
+###############################################################################
+# Guardrails, Judges, Supervisor
+###############################################################################
+ENABLE_INPUT_GUARDRAILS=true
+ENABLE_OUTPUT_GUARDRAILS=true
+ENABLE_JUDGES=true
+ENABLE_SUPERVISOR=true
+ENABLE_OUTPUT_SUPERVISOR=true
+ENABLE_PARALLEL_GUARDRAILS=true
+GUARDRAILS_FAIL_FAST=true
+OUTPUT_SUPERVISOR_MAX_RETRIES=3
+GUARDRAILS_CONFIG_PATH=./config/guardrails.yaml
+JUDGES_CONFIG_PATH=./config/judges.yaml
+PROMPT_POLICY_PATH=./config/prompt_policy.yaml
+
+###############################################################################
+# Gateway de canais
+###############################################################################
+DEFAULT_CHANNEL=web
+ENABLE_VOICE_ADAPTER=true
+ENABLE_WHATSAPP_ADAPTER=true
+ENABLE_TEXT_ADAPTER=true
+
+#################################################
+# ENTERPRISE ROUTING
+#################################################
+# Arquivo YAML com intents, keywords, políticas de estado e fallback.
+ROUTING_CONFIG_PATH=./config/routing.yaml
+# true = usa LLM para classificar quando keywords/estado não resolverem.
+# Em produção, costuma ser útil; em desenvolvimento, false evita custo e latência.
+ENABLE_LLM_ROUTER=true
+
+###############################################################################
+# MCP / Tools
+###############################################################################
+ENABLE_MCP_TOOLS=true
+MCP_SERVERS_CONFIG_PATH=./config/mcp_servers.yaml
+TOOLS_CONFIG_PATH=./config/tools.yaml
+TOOL_POLICIES_PATH=./config/tool_policies.yaml
+MCP_TOOL_TIMEOUT_SECONDS=30
+
+# router = EnterpriseRouter seleciona um agente; supervisor = pode acionar múltiplos agentes
+ROUTING_MODE=router
+
+# Usage/cost accounting
+USAGE_REPOSITORY_PROVIDER=sqlite
+IDENTITY_CONFIG_PATH=./config/identity.yaml
+MCP_PARAMETER_MAPPING_PATH=./config/mcp_parameter_mapping.yaml
+
+# -----------------------------------------------------------------------------
+# ConversationSummaryMemory / compressão de contexto conversacional
+# -----------------------------------------------------------------------------
+ENABLE_CONVERSATION_SUMMARY_MEMORY=true
+MEMORY_CONTEXT_STRATEGY=summary
+MEMORY_HISTORY_LIMIT=80
+MEMORY_RECENT_MESSAGES_LIMIT=8
+MEMORY_SUMMARY_TRIGGER_MESSAGES=20
+MEMORY_MAX_SUMMARY_CHARS=6000
+MEMORY_SUMMARY_USE_LLM=true
+MEMORY_INJECT_RECENT_MESSAGES=true
+MEMORY_INJECT_SUMMARY=true
+
+###############################################################################
+# MCP Gateway
+###############################################################################
+# true = framework routes tool calls to the dedicated MCP Gateway.
+# false = framework calls MCP servers directly from mcp_servers.yaml.
+MCP_GATEWAY_ENABLED=true
+MCP_GATEWAY_URL=http://localhost:8300
+MCP_GATEWAY_TIMEOUT_SECONDS=60
+# MCP_GATEWAY_TOKEN=
+MCP_GATEWAY_AGENT_ID=telecom_contas
+MCP_GATEWAY_TENANT_ID=default
+
+###############################################################################
+# LONG-TERM MEMORY
+###############################################################################
+ENABLE_LONG_TERM_MEMORY=true
+LONG_TERM_MEMORY_PROVIDER=sqlite
+LONG_TERM_MEMORY_SQLITE_PATH=./data/agent_framework.db
+LONG_TERM_MEMORY_TABLE=agentfw_long_term_memory
+# For Autonomous/Oracle, defaults to ${ADB_TABLE_PREFIX}_LONG_TERM_MEMORY
+# LONG_TERM_MEMORY_ORACLE_TABLE=AGENTFW_LONG_TERM_MEMORY
+LONG_TERM_MEMORY_MAX_CONTEXT_ITEMS=20
+LONG_TERM_MEMORY_MIN_CONFIDENCE=0.70
+LONG_TERM_MEMORY_AUTO_EXTRACT=true
+LONG_TERM_MEMORY_INJECT_CONTEXT=true

Tuning-Performance/Normal/templates/agent_template_backend_day_zero/config/mcp_parameter_mapping.yaml

@@ -60,6 +60,18 @@ mcp_parameter_mapping:
             mensagem.
           pattern: (?i)\\b(?:pedido|order)\\s*[:#-]?\\s*([A-Z0-9-]+)\\b
           group: 1
+    cancelar_pedido:
+      map:
+        session_key: session_id
+      extract:
+        order_id:
+          from: message
+          type: string
+          strategy: hybrid
+          description: Extraia somente o identificador do pedido informado explicitamente pelo usuário. Retorne null quando não houver identificador de pedido na mensagem.
+          pattern: (?i)\\b(?:pedido|order)\\s*[:#-]?\\s*([A-Z0-9-]+)\\b
+          group: 1
+
     solicitar_troca:
       map:
         session_key: session_id

Tuning-Performance/Normal/templates/agent_template_backend_day_zero/config/routing.yaml

@@ -79,6 +79,25 @@ intents:
       - Quero saber sobre meu pacote de internet.
       - Tenho roaming internacional?
 
+
+  - name: retail_order_cancel
+    domain: retail
+    agent: orders_agent
+    description: Cancelamento explícito de pedido ou compra.
+    priority: 20
+    mcp_tools:
+      - consultar_pedido
+      - cancelar_pedido
+    keywords:
+      - cancelar pedido
+      - cancelamento do pedido
+      - cancelar a compra
+      - cancelar compra
+    examples:
+      - Quero cancelar meu pedido.
+      - Cancele o pedido.
+      - Quero cancelar a compra.
+
   - name: retail_order_tracking
     domain: retail
     agent: orders_agent

Tuning-Performance/Normal/templates/agent_template_backend_day_zero/config/tool_policies.yaml

@@ -7,6 +7,12 @@ defaults:
   require_confirmation: false
 
 tool_policies:
+  cancelar_pedido:
+    operation_type: transactional
+    require_confirmation: true
+    requires: [order_id]
+
+
   solicitar_troca:
     operation_type: transactional
     require_confirmation: true

Tuning-Performance/Normal/templates/agent_template_backend_day_zero/config/tools.yaml

@@ -61,6 +61,23 @@ tools:
     - rastreamento
     - transportadora
     - previsão
+  cancelar_pedido:
+    description: Simula o cancelamento de um pedido de varejo.
+    mcp_server: retail
+    enabled: true
+    tool_type: action
+    requires:
+    - order_id
+    confirmation_required: true
+    args_schema:
+      order_id: string
+    selection_keywords:
+    - cancelar pedido
+    - cancelamento do pedido
+    - cancelar compra
+    - cancelar a compra
+
+
   solicitar_troca:
     description: Simula abertura de solicitação de troca.
     mcp_server: retail

Tuning-Performance/Offline_Workflow_Regression/README.md

@@ -0,0 +1,21 @@
+# Offline Workflow Regression
+
+O backend de produção de `WorkflowRuntime` continua sendo **LangGraph**. A ausência do pacote `langgraph` em produção é erro de configuração.
+
+Para builders restritos/offline, o runtime aceita `allow_deterministic_fallback=True`. Esse modo é deliberadamente opt-in e existe somente para exercitar a DSL do framework (actions, edges, condições, pause/resume e trace) quando a dependência externa não pode ser instalada. Ele não é selecionado automaticamente.
+
+Exemplo de teste:
+
+```python
+runtime = WorkflowRuntime(
+    repository,
+    actions=registry,
+    allow_deterministic_fallback=True,
+)
+first = await runtime.arun("workflow", payload)
+assert first.status == "PAUSED"
+final = await runtime.aresume("workflow", first.execution_id, {"resposta": "SIM"})
+assert final.status == "COMPLETED"
+```
+
+O objetivo é não transformar indisponibilidade de rede/PyPI em `pytest.skip`, sem mascarar o requisito de LangGraph do runtime de produção.

Tuning-Performance/Pause_Resume_Workflow/IMPLEMENTACAO_PAUSE_RESUME_LANGGRAPH.md

@@ -0,0 +1,15 @@
+# Implementação no framework
+
+A evolução adiciona `WorkflowPause` e `WorkflowExpectedInput` ao modelo de workflow e mantém o LangGraph como detalhe interno de `WorkflowRuntime`.
+
+O runtime aceita condições declarativas `all`, `any`, `not`, `eq`, `neq`, `exists`, `path/equals`, `path/not_equals` e `path/in`. Em um nó com `pause`, a action é executada primeiro e a interrupção ocorre em um nó técnico separado. Na retomada, `langgraph.types.Command(resume=...)` injeta o valor esperado e segue para `resume_from` sem repetir a action que precedeu o pause.
+
+`FrameworkStateGraph` é a facade para aplicações que ainda precisam compor um grafo de agentes; templates oficiais devem usar a facade e não importar LangGraph diretamente.
+
+## Preservação de estado em falhas posteriores
+
+O `WorkflowRuntime` também preserva o último snapshot persistido do LangGraph quando uma action posterior falha. O resultado `FAILED` contém `output`, `state` e `trace` dos nodes que já terminaram com sucesso.
+
+Isso é necessário para workflows transacionais: por exemplo, se um protocolo foi criado e uma chamada posterior falha, o chamador ainda recebe o `protocol_number` persistido e pode executar recuperação/idempotência sem repetir o primeiro side effect.
+
+O runtime não transforma falha em sucesso e não reexecuta automaticamente a action; ele apenas preserva a evidência durável já existente no checkpointer.

Tuning-Performance/Pause_Resume_Workflow/README.md

@@ -0,0 +1,34 @@
+# Pause/Resume Workflow — LangGraph encapsulado pelo Agent Framework OCI
+
+Este exemplo demonstra uma capability genérica do `agent_framework_oci`: workflows determinísticos podem interromper a execução para obter uma resposta do cliente e retomar posteriormente pelo mesmo `execution_id`, sem que o domínio importe ou monte um `StateGraph`.
+
+## Conceito
+
+A aplicação declara o workflow em YAML. `WorkflowRuntime` transforma a definição em LangGraph internamente, utiliza o checkpointer configurado pelo framework e expõe somente:
+
+- `arun(name, payload)` — inicia ou executa o workflow;
+- `aresume(name, execution_id, value)` — retoma o workflow pausado;
+- `WorkflowRunResult.status` — `PAUSED`, `COMPLETED` ou `FAILED`.
+
+O `pause` é compilado em um nó separado da action anterior. Isto impede que uma action com efeito colateral seja executada novamente quando o cliente responde.
+
+## Executar
+
+A partir da raiz do exemplo, com as dependências do framework instaladas:
+
+```bash
+python -m app.demo
+pytest -q
+```
+
+## YAML
+
+`workflows/confirmacao.v1.yaml` mostra `expected_input`, normalização, valores permitidos e `resume_from`.
+
+## Persistência
+
+O exemplo usa `MemorySaver` apenas para ser autocontido. Em aplicações reais use `create_langgraph_checkpointer(settings)`. Assim `execution_id` é o `thread_id` do LangGraph e a retomada sobrevive a processos/replicas conforme o provider configurado (por exemplo Autonomous Database).
+
+## Regra arquitetural
+
+Código de domínio não deve importar `langgraph.graph.StateGraph`. Para grafos de agentes use `FrameworkStateGraph`; para workflows determinísticos de negócio use `WorkflowRuntime`.

Tuning-Performance/Pause_Resume_Workflow/agent_template_backend_pause_resume/app/demo.py

@@ -0,0 +1,56 @@
+from __future__ import annotations
+
+import asyncio
+from pathlib import Path
+
+from agent_framework.workflows import FileWorkflowRepository, WorkflowActionRegistry, WorkflowRuntime
+
+ROOT = Path(__file__).resolve().parents[1]
+
+
+def build_runtime(*, offline_test_fallback: bool = False) -> WorkflowRuntime:
+    actions = WorkflowActionRegistry()
+
+    async def preparar(params, state):
+        return {"assunto": params.get("assunto") or "operação"}
+
+    async def perguntar(params, state):
+        return {"mensagem": f"Deseja confirmar {params['assunto']}?"}
+
+    async def decidir(params, state):
+        return {
+            "mensagem": "Operação confirmada." if params["resposta"] == "SIM" else "Operação cancelada.",
+            "confirmado": params["resposta"] == "SIM",
+        }
+
+    actions.register("preparar_operacao", preparar)
+    actions.register("montar_pergunta", perguntar)
+    actions.register("registrar_decisao", decidir)
+
+    checkpointer = None
+    if not offline_test_fallback:
+        # Produção/exemplo real continua usando LangGraph + checkpointer. O import
+        # fica aqui para que a regressão offline do repositório não dependa de rede.
+        from langgraph.checkpoint.memory import MemorySaver
+        checkpointer = MemorySaver()
+
+    return WorkflowRuntime(
+        FileWorkflowRepository(ROOT / "workflows"),
+        actions=actions,
+        checkpointer=checkpointer,
+        allow_deterministic_fallback=offline_test_fallback,
+    )
+
+
+async def main() -> None:
+    runtime = build_runtime()
+    first = await runtime.arun("confirmacao", {"assunto": "a alteração do plano"})
+    print(first.model_dump(mode="json"))
+    assert first.status == "PAUSED"
+    resumed = await runtime.aresume("confirmacao", first.execution_id, {"resposta_usuario": "sim"})
+    print(resumed.model_dump(mode="json"))
+    assert resumed.status == "COMPLETED"
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

Tuning-Performance/Pause_Resume_Workflow/agent_template_backend_pause_resume/tests/test_pause_resume.py

@@ -0,0 +1,22 @@
+from __future__ import annotations
+
+import pytest
+
+from app.demo import build_runtime
+
+
+@pytest.mark.asyncio
+async def test_pause_resume_does_not_repeat_previous_action():
+    # Regressão offline: exercita a mesma DSL/WorkflowRuntime sem exigir download
+    # de LangGraph no builder. Produção continua usando build_runtime() default.
+    runtime = build_runtime(offline_test_fallback=True)
+    first = await runtime.arun("confirmacao", {"assunto": "o cancelamento"})
+    assert first.status == "PAUSED"
+    assert first.pause["expected_input"]["key"] == "resposta_usuario"
+    before = [item for item in first.trace if item.get("action") == "preparar_operacao"]
+    assert len(before) == 1
+    resumed = await runtime.aresume("confirmacao", first.execution_id, {"resposta_usuario": "SIM"})
+    assert resumed.status == "COMPLETED"
+    after = [item for item in resumed.trace if item.get("action") == "preparar_operacao"]
+    assert len(after) == 1
+    assert resumed.state["vars"]["decidir"]["confirmado"] is True

Tuning-Performance/Pause_Resume_Workflow/agent_template_backend_pause_resume/workflows/confirmacao.active.yaml

@@ -0,0 +1 @@
+version: 1

Tuning-Performance/Pause_Resume_Workflow/agent_template_backend_pause_resume/workflows/confirmacao.v1.yaml

@@ -0,0 +1,32 @@
+name: confirmacao
+version: 1
+start: preparar
+nodes:
+  - id: preparar
+    action: preparar_operacao
+    input:
+      assunto: $.input.assunto
+  - id: perguntar
+    action: montar_pergunta
+    input:
+      assunto: $.vars.preparar.assunto
+    pause:
+      enabled: true
+      return_from: $.output.mensagem
+      expected_input:
+        key: resposta_usuario
+        allowed_values: [SIM, NAO]
+        normalize: upper_strip
+      resume_from: decidir
+  - id: decidir
+    action: registrar_decisao
+    input:
+      resposta: $.input.resposta_usuario
+      assunto: $.vars.preparar.assunto
+edges:
+  - from: preparar
+    to: perguntar
+  - from: perguntar
+    to: END
+  - from: decidir
+    to: END

Tuning-Performance/README.md

@@ -6,3 +6,5 @@ Variantes e documentos de referência para comparar funcionalidades e impacto de
 - `Route_Stickness`: continuidade de rota, handoff e políticas transacionais conversacionais.
 - `Long_Term_Memory`: memória de longo prazo.
 - `Deterministic_Transactional_Workflow`: transações multi-etapas executadas por workflow LangGraph determinístico após clarification e confirmação.
+- `Transaction_Evidence`: persistência e correlação de resultados transacionais como evidência operacional para turnos posteriores e groundedness.
+- `Transaction_Pre_Validation`: pré-validação MCP side-effect-free antes da confirmação transacional; regras de elegibilidade permanecem no domínio.

Tuning-Performance/Route_Stickness/templates/agent_template_backend/.env.example

@@ -0,0 +1,207 @@
+###############################################################################
+# AI AGENT PLATFORM - CONFIGURAÇÃO ÚNICA
+# Este arquivo é lido por Pydantic Settings no framework e no backend template.
+###############################################################################
+
+APP_NAME=ai-agent-template
+APP_ENV=local
+LOG_LEVEL=INFO
+API_HOST=0.0.0.0
+API_PORT=8000
+CORS_ORIGINS=http://localhost:5173,http://127.0.0.1:5173
+
+###############################################################################
+# LLM - OCI Generative AI como provider principal
+###############################################################################
+# Opções: mock, oci_openai, oci_sdk, openai_compatible
+LLM_PROVIDER=oci_openai
+LLM_TEMPERATURE=0.2
+LLM_MAX_TOKENS=2048
+LLM_TIMEOUT_SECONDS=120
+
+# OCI OpenAI-compatible endpoint
+OCI_GENAI_BASE_URL=https://inference.generativeai.us-chicago-1.oci.oraclecloud.com/openai/v1
+OCI_GENAI_MODEL=openai.gpt-4.1
+OCI_GENAI_API_KEY=sk-ph3FgX6ph3FgX6ph3FgX6ph3FgX6ph3FgX6ph3FgX6
+OCI_GENAI_PROJECT_OCID=
+
+# OCI SDK / signer / profiles
+OCI_CONFIG_FILE=~/.oci/config
+OCI_PROFILE=DEFAULT
+OCI_COMPARTMENT_ID=ocid1.compartment.oc1..aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa
+OCI_REGION=us-chicago-1
+
+###############################################################################
+# Persistência
+###############################################################################
+# Opções: memory, autonomous, mongodb
+SESSION_REPOSITORY_PROVIDER=sqlite
+MEMORY_REPOSITORY_PROVIDER=sqlite
+CHECKPOINT_REPOSITORY_PROVIDER=sqlite
+SQLITE_DB_PATH=./data/agent_framework.db
+
+# Autonomous Database
+ADB_USER=admin
+ADB_PASSWORD=fjhsdf04954hf
+ADB_DSN=oradb23aidev_high
+ADB_WALLET_LOCATION=/ORACLE/DEFAULT/Wallet_ORADB23aiDev
+ADB_WALLET_PASSWORD=fjhsdf04954hf
+ADB_TABLE_PREFIX=AGENTFW
+
+# MongoDB - também pode representar Autonomous usando API compatível com Mongo, se habilitada no ambiente
+MONGODB_URI=mongodb://mongo:mongopassword@localhost:27017
+MONGODB_DATABASE=agent_platform
+
+# Redis
+REDIS_URL=redis://localhost:6379/0
+ENABLE_REDIS_CACHE=false
+
+###############################################################################
+# RAG / Vector / Graph
+###############################################################################
+VECTOR_STORE_PROVIDER=sqlite
+GRAPH_STORE_PROVIDER=sqlite
+RAG_TOP_K=5
+EMBEDDING_PROVIDER=mock
+OCI_EMBEDDING_MODEL=cohere.embed-multilingual-v3.0
+RAG_FILE_GLOBS=*.md,*.txt,*.yaml,*.yml,*.json
+
+###############################################################################
+# Observabilidade
+###############################################################################
+ENABLE_LANGFUSE=true
+LANGFUSE_TRACE_MODE=compact # Opcional: verbose, compact
+LANGFUSE_PUBLIC_KEY=pk-lf-2f9da109-5b0f-4c78-b61d-9598ed787eba
+LANGFUSE_SECRET_KEY=sk-lf-a4cb0cdd-f2ea-4468-9911-cebeb91ba944
+LANGFUSE_HOST=http://localhost:3005
+ENABLE_OTEL=false
+OTEL_EXPORTER_OTLP_ENDPOINT=
+OTEL_SERVICE_NAME=ai-agent-template
+ENABLE_LANGFUSE_OPENAI_AUTO_INSTRUMENTATION=true
+
+###############################################################################
+# Analytics / Observer corporativo
+###############################################################################
+# Quando true, AgentObserver publica eventos IC.*, NOC.* e GRL.* nos providers abaixo.
+ENABLE_ANALYTICS=false
+# Providers aceitos: oci_streaming,pubsub,noop
+ANALYTICS_PROVIDERS=pubsub
+# Compatibilidade FIRST/TIM: pode informar AGENT_PUBSUB_TOPIC diretamente.
+AGENT_PUBSUB_TOPIC=
+GCP_PUBSUB_TOPIC_PATH=
+GCP_PROJECT_ID=
+GCP_PUBSUB_TOPIC=
+GCP_PUBSUB_TIMEOUT_SECONDS=30
+# Credencial GCP segue padrão Google:
+# GOOGLE_APPLICATION_CREDENTIALS=/secrets/gcp-service-account.json
+
+###############################################################################
+# OCI Streaming
+###############################################################################
+ENABLE_OCI_STREAMING=false
+OCI_STREAM_ENDPOINT=
+OCI_STREAM_OCID=
+OCI_STREAM_PARTITION_KEY=agent-events
+
+###############################################################################
+# Guardrails, Judges, Supervisor
+###############################################################################
+ENABLE_INPUT_GUARDRAILS=true
+ENABLE_OUTPUT_GUARDRAILS=true
+ENABLE_JUDGES=true
+ENABLE_SUPERVISOR=true
+ENABLE_OUTPUT_SUPERVISOR=true
+ENABLE_PARALLEL_GUARDRAILS=true
+GUARDRAILS_FAIL_FAST=true
+OUTPUT_SUPERVISOR_MAX_RETRIES=3
+GUARDRAILS_CONFIG_PATH=./config/guardrails.yaml
+JUDGES_CONFIG_PATH=./config/judges.yaml
+PROMPT_POLICY_PATH=./config/prompt_policy.yaml
+
+###############################################################################
+# Gateway de canais
+###############################################################################
+DEFAULT_CHANNEL=web
+# embedded = backend may parse simple/native channel payloads.
+# external = backend only accepts GatewayRequest normalized by an external Channel Gateway.
+FRAMEWORK_CHANNEL_INPUT_MODE=embedded
+ENABLE_VOICE_ADAPTER=true
+ENABLE_WHATSAPP_ADAPTER=true
+ENABLE_TEXT_ADAPTER=true
+
+#################################################
+# ENTERPRISE ROUTING
+#################################################
+# Arquivo YAML com intents, keywords, políticas de estado e fallback.
+ROUTING_CONFIG_PATH=./config/routing.yaml
+# true = usa LLM para classificar quando keywords/estado não resolverem.
+# Em produção, costuma ser útil; em desenvolvimento, false evita custo e latência.
+ENABLE_LLM_ROUTER=true
+
+# Semantic route stickiness (optional).
+# Uses a lightweight LLM profile to decide only CONTINUE vs ROUTE.
+# There are no regexes or deterministic language rules.
+ENABLE_ROUTE_STICKINESS=true
+ROUTE_STICKINESS_LLM_PROFILE=route_continuity
+ROUTE_STICKINESS_CONFIDENCE_THRESHOLD=0.90
+ROUTE_STICKINESS_HISTORY_TURNS=2
+ROUTE_STICKINESS_MAX_TOKENS=80
+HUMAN_HANDOFF_MESSAGE=Vou encaminhar seu atendimento para uma pessoa.
+END_SESSION_MESSAGE=Atendimento encerrado. Obrigado pelo contato.
+SESSION_ALREADY_ENDED_MESSAGE=Este atendimento já foi encerrado. Inicie uma nova sessão para continuar.
+
+###############################################################################
+# MCP / Tools
+###############################################################################
+ENABLE_MCP_TOOLS=true
+MCP_SERVERS_CONFIG_PATH=./config/mcp_servers.yaml
+TOOLS_CONFIG_PATH=./config/tools.yaml
+TOOL_POLICIES_PATH=./config/tool_policies.yaml
+MCP_TOOL_TIMEOUT_SECONDS=30
+
+# router = EnterpriseRouter seleciona um agente; supervisor = pode acionar múltiplos agentes
+ROUTING_MODE=router
+
+# Usage/cost accounting
+USAGE_REPOSITORY_PROVIDER=sqlite
+IDENTITY_CONFIG_PATH=./config/identity.yaml
+MCP_PARAMETER_MAPPING_PATH=./config/mcp_parameter_mapping.yaml
+
+# -----------------------------------------------------------------------------
+# ConversationSummaryMemory / compressão de contexto conversacional
+# -----------------------------------------------------------------------------
+ENABLE_CONVERSATION_SUMMARY_MEMORY=true
+MEMORY_CONTEXT_STRATEGY=summary
+MEMORY_HISTORY_LIMIT=80
+MEMORY_RECENT_MESSAGES_LIMIT=8
+MEMORY_SUMMARY_TRIGGER_MESSAGES=20
+MEMORY_MAX_SUMMARY_CHARS=6000
+MEMORY_SUMMARY_USE_LLM=true
+MEMORY_INJECT_RECENT_MESSAGES=true
+MEMORY_INJECT_SUMMARY=true
+
+###############################################################################
+# MCP Gateway
+###############################################################################
+# true = framework routes tool calls to the dedicated MCP Gateway.
+# false = framework calls MCP servers directly from mcp_servers.yaml.
+MCP_GATEWAY_ENABLED=true
+MCP_GATEWAY_URL=http://localhost:8300
+MCP_GATEWAY_TIMEOUT_SECONDS=60
+# MCP_GATEWAY_TOKEN=
+MCP_GATEWAY_AGENT_ID=telecom_contas
+MCP_GATEWAY_TENANT_ID=default
+
+###############################################################################
+# LONG-TERM MEMORY
+###############################################################################
+ENABLE_LONG_TERM_MEMORY=true
+LONG_TERM_MEMORY_PROVIDER=sqlite
+LONG_TERM_MEMORY_SQLITE_PATH=./data/agent_framework.db
+LONG_TERM_MEMORY_TABLE=agentfw_long_term_memory
+# For Autonomous/Oracle, defaults to ${ADB_TABLE_PREFIX}_LONG_TERM_MEMORY
+# LONG_TERM_MEMORY_ORACLE_TABLE=AGENTFW_LONG_TERM_MEMORY
+LONG_TERM_MEMORY_MAX_CONTEXT_ITEMS=20
+LONG_TERM_MEMORY_MIN_CONFIDENCE=0.70
+LONG_TERM_MEMORY_AUTO_EXTRACT=true
+LONG_TERM_MEMORY_INJECT_CONTEXT=true

Tuning-Performance/Route_Stickness/templates/agent_template_backend/app/agents/runtime.py

@@ -3,5 +3,8 @@ from __future__ import annotations
 # Compatibilidade local do template/backend.
 # A implementação oficial agora fica no framework para evitar duplicação entre agentes.
 from agent_framework.runtime import AgentRuntimeMixin, MessageBuilder, RuntimeContext
+from app.presentation import register_tool_renderers
+
+register_tool_renderers()
 
 __all__ = ["AgentRuntimeMixin", "MessageBuilder", "RuntimeContext"]

Tuning-Performance/Route_Stickness/templates/agent_template_backend/app/presentation/__init__.py

@@ -0,0 +1,3 @@
+from .tool_renderers import register_tool_renderers
+
+__all__ = ["register_tool_renderers"]

Tuning-Performance/Route_Stickness/templates/agent_template_backend/app/presentation/tool_renderers.py

@@ -0,0 +1,74 @@
+from __future__ import annotations
+
+from typing import Any
+
+from agent_framework.presentation import register_tool_response_renderer
+
+
+def _money_brl(value: Any) -> str:
+    try:
+        return f"{float(value):.2f}".replace(".", ",")
+    except (TypeError, ValueError):
+        return str(value)
+
+
+def render_telecom_invoice(*, tool_name: str, result: dict[str, Any], state: dict[str, Any], agent_label: str) -> str | None:
+    return f"[{agent_label}] Fatura consultada: {result}."
+
+
+def render_telecom_plan(*, tool_name: str, result: dict[str, Any], state: dict[str, Any], agent_label: str) -> str | None:
+    plano = result.get("plano")
+    if plano is None:
+        return None
+    parts = [f"[{agent_label}] Seu plano é {plano}"]
+    internet_gb = result.get("internet_gb")
+    status = result.get("status")
+    if internet_gb is not None:
+        parts.append(f"com {internet_gb} GB")
+    if status is not None:
+        parts.append(f"status {status}")
+    return ", ".join(parts) + "."
+
+
+def render_retail_order(*, tool_name: str, result: dict[str, Any], state: dict[str, Any], agent_label: str) -> str | None:
+    order_id = result.get("order_id")
+    status = result.get("status")
+    if order_id is None or status is None:
+        return None
+    lines = [f"[{agent_label}] Pedido {order_id}: status {status}."]
+    total = result.get("valor_total")
+    if total is not None:
+        lines.append(f"Valor total: R$ {_money_brl(total)}.")
+    items = result.get("itens") or []
+    rendered_items: list[str] = []
+    if isinstance(items, list):
+        for item in items:
+            if isinstance(item, dict):
+                value = item.get("descricao") or item.get("nome") or item.get("sku")
+            else:
+                value = item
+            if value not in (None, ""):
+                rendered_items.append(str(value))
+    if rendered_items:
+        lines.append("Itens: " + "; ".join(rendered_items) + ".")
+    return " ".join(lines)
+
+
+def render_retail_delivery(*, tool_name: str, result: dict[str, Any], state: dict[str, Any], agent_label: str) -> str | None:
+    order_id = result.get("order_id")
+    transportadora = result.get("transportadora")
+    codigo = result.get("codigo_rastreio")
+    previsao = result.get("previsao_entrega")
+    if any(v is None for v in (order_id, transportadora, codigo, previsao)):
+        return None
+    return (
+        f"[{agent_label}] Entrega do pedido {order_id}: transportadora {transportadora}, "
+        f"rastreio {codigo}, previsão {previsao}."
+    )
+
+
+def register_tool_renderers() -> None:
+    register_tool_response_renderer("telecom.invoice", render_telecom_invoice)
+    register_tool_response_renderer("telecom.plan", render_telecom_plan)
+    register_tool_response_renderer("retail.order", render_retail_order)
+    register_tool_response_renderer("retail.delivery", render_retail_delivery)

Tuning-Performance/Route_Stickness/templates/agent_template_backend/config/mcp_parameter_mapping.yaml

@@ -60,6 +60,18 @@ mcp_parameter_mapping:
             mensagem.
           pattern: (?i)\\b(?:pedido|order)\\s*[:#-]?\\s*([A-Z0-9-]+)\\b
           group: 1
+    cancelar_pedido:
+      map:
+        session_key: session_id
+      extract:
+        order_id:
+          from: message
+          type: string
+          strategy: hybrid
+          description: Extraia somente o identificador do pedido informado explicitamente pelo usuário. Retorne null quando não houver identificador de pedido na mensagem.
+          pattern: (?i)\\b(?:pedido|order)\\s*[:#-]?\\s*([A-Z0-9-]+)\\b
+          group: 1
+
     solicitar_troca:
       map:
         session_key: session_id

Tuning-Performance/Route_Stickness/templates/agent_template_backend/config/routing.yaml

@@ -79,6 +79,25 @@ intents:
       - Quero saber sobre meu pacote de internet.
       - Tenho roaming internacional?
 
+
+  - name: retail_order_cancel
+    domain: retail
+    agent: orders_agent
+    description: Cancelamento explícito de pedido ou compra.
+    priority: 20
+    mcp_tools:
+      - consultar_pedido
+      - cancelar_pedido
+    keywords:
+      - cancelar pedido
+      - cancelamento do pedido
+      - cancelar a compra
+      - cancelar compra
+    examples:
+      - Quero cancelar meu pedido.
+      - Cancele o pedido.
+      - Quero cancelar a compra.
+
   - name: retail_order_tracking
     domain: retail
     agent: orders_agent

Tuning-Performance/Route_Stickness/templates/agent_template_backend/config/tool_policies.yaml

@@ -7,6 +7,12 @@ defaults:
   require_confirmation: false
 
 tool_policies:
+  cancelar_pedido:
+    operation_type: transactional
+    require_confirmation: true
+    requires: [order_id]
+
+
   solicitar_troca:
     operation_type: transactional
     require_confirmation: true

Tuning-Performance/Route_Stickness/templates/agent_template_backend/config/tools.yaml

@@ -10,6 +10,9 @@ tools:
     - fatura
     - conta
     - boleto
+    response:
+      mode: renderer
+      renderer: telecom.invoice
   consultar_pagamentos:
     description: Consulta histórico de pagamentos do cliente.
     mcp_server: telecom
@@ -28,6 +31,9 @@ tools:
       asset_id: string
     selection_keywords:
     - plano
+    response:
+      mode: renderer
+      renderer: telecom.plan
   listar_servicos:
     description: Lista serviços ativos e adicionais VAS.
     mcp_server: telecom
@@ -49,6 +55,9 @@ tools:
     - consultar pedido
     - status do pedido
     - pedido
+    response:
+      mode: renderer
+      renderer: retail.order
   consultar_entrega:
     description: Consulta entrega e rastreamento do pedido.
     mcp_server: retail
@@ -61,6 +70,26 @@ tools:
     - rastreamento
     - transportadora
     - previsão
+    response:
+      mode: renderer
+      renderer: retail.delivery
+  cancelar_pedido:
+    description: Simula o cancelamento de um pedido de varejo.
+    mcp_server: retail
+    enabled: true
+    tool_type: action
+    requires:
+    - order_id
+    confirmation_required: true
+    args_schema:
+      order_id: string
+    selection_keywords:
+    - cancelar pedido
+    - cancelamento do pedido
+    - cancelar compra
+    - cancelar a compra
+
+
   solicitar_troca:
     description: Simula abertura de solicitação de troca.
     mcp_server: retail

Tuning-Performance/Route_Stickness/templates/agent_template_backend_day_zero/.env.example

@@ -0,0 +1,202 @@
+###############################################################################
+# AI AGENT PLATFORM - CONFIGURAÇÃO ÚNICA
+# Este arquivo é lido por Pydantic Settings no framework e no backend template.
+###############################################################################
+
+APP_NAME=ai-agent-template
+APP_ENV=local
+LOG_LEVEL=INFO
+API_HOST=0.0.0.0
+API_PORT=8000
+CORS_ORIGINS=http://localhost:5173,http://127.0.0.1:5173
+
+###############################################################################
+# LLM - OCI Generative AI como provider principal
+###############################################################################
+# Opções: mock, oci_openai, oci_sdk, openai_compatible
+LLM_PROVIDER=oci_openai
+LLM_TEMPERATURE=0.2
+LLM_MAX_TOKENS=2048
+LLM_TIMEOUT_SECONDS=120
+
+# OCI OpenAI-compatible endpoint
+OCI_GENAI_BASE_URL=https://inference.generativeai.us-chicago-1.oci.oraclecloud.com/openai/v1
+OCI_GENAI_MODEL=openai.gpt-4.1
+OCI_GENAI_API_KEY=sk-ph3FgX6ph3FgX6ph3FgX6ph3FgX6ph3FgX6ph3FgX6
+OCI_GENAI_PROJECT_OCID=
+
+# OCI SDK / signer / profiles
+OCI_CONFIG_FILE=~/.oci/config
+OCI_PROFILE=DEFAULT
+OCI_COMPARTMENT_ID=ocid1.compartment.oc1..aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa
+OCI_REGION=us-chicago-1
+
+###############################################################################
+# Persistência
+###############################################################################
+# Opções: memory, autonomous, mongodb
+SESSION_REPOSITORY_PROVIDER=sqlite
+MEMORY_REPOSITORY_PROVIDER=sqlite
+CHECKPOINT_REPOSITORY_PROVIDER=sqlite
+SQLITE_DB_PATH=./data/agent_framework.db
+
+# Autonomous Database
+ADB_USER=admin
+ADB_PASSWORD=fjhsdf04954hf
+ADB_DSN=oradb23aidev_high
+ADB_WALLET_LOCATION=/ORACLE/DEFAULT/Wallet_ORADB23aiDev
+ADB_WALLET_PASSWORD=fjhsdf04954hf
+ADB_TABLE_PREFIX=AGENTFW
+
+# MongoDB - também pode representar Autonomous usando API compatível com Mongo, se habilitada no ambiente
+MONGODB_URI=mongodb://mongo:mongopassword@localhost:27017
+MONGODB_DATABASE=agent_platform
+
+# Redis
+REDIS_URL=redis://localhost:6379/0
+ENABLE_REDIS_CACHE=false
+
+###############################################################################
+# RAG / Vector / Graph
+###############################################################################
+VECTOR_STORE_PROVIDER=sqlite
+GRAPH_STORE_PROVIDER=sqlite
+RAG_TOP_K=5
+EMBEDDING_PROVIDER=mock
+OCI_EMBEDDING_MODEL=cohere.embed-multilingual-v3.0
+RAG_FILE_GLOBS=*.md,*.txt,*.yaml,*.yml,*.json
+
+###############################################################################
+# Observabilidade
+###############################################################################
+ENABLE_LANGFUSE=true
+LANGFUSE_TRACE_MODE=compact # Opcional: verbose, compact
+LANGFUSE_PUBLIC_KEY=pk-lf-2f9da109-5b0f-4c78-b61d-9598ed787eba
+LANGFUSE_SECRET_KEY=sk-lf-a4cb0cdd-f2ea-4468-9911-cebeb91ba944
+LANGFUSE_HOST=http://localhost:3005
+ENABLE_OTEL=false
+OTEL_EXPORTER_OTLP_ENDPOINT=
+OTEL_SERVICE_NAME=ai-agent-template
+ENABLE_LANGFUSE_OPENAI_AUTO_INSTRUMENTATION=true
+
+###############################################################################
+# Analytics / Observer corporativo
+###############################################################################
+# Quando true, AgentObserver publica eventos IC.*, NOC.* e GRL.* nos providers abaixo.
+ENABLE_ANALYTICS=false
+# Providers aceitos: oci_streaming,pubsub,noop
+ANALYTICS_PROVIDERS=pubsub
+# Compatibilidade FIRST/TIM: pode informar AGENT_PUBSUB_TOPIC diretamente.
+AGENT_PUBSUB_TOPIC=
+GCP_PUBSUB_TOPIC_PATH=
+GCP_PROJECT_ID=
+GCP_PUBSUB_TOPIC=
+GCP_PUBSUB_TIMEOUT_SECONDS=30
+# Credencial GCP segue padrão Google:
+# GOOGLE_APPLICATION_CREDENTIALS=/secrets/gcp-service-account.json
+
+###############################################################################
+# OCI Streaming
+###############################################################################
+ENABLE_OCI_STREAMING=false
+OCI_STREAM_ENDPOINT=
+OCI_STREAM_OCID=
+OCI_STREAM_PARTITION_KEY=agent-events
+
+###############################################################################
+# Guardrails, Judges, Supervisor
+###############################################################################
+ENABLE_INPUT_GUARDRAILS=true
+ENABLE_OUTPUT_GUARDRAILS=true
+ENABLE_JUDGES=true
+ENABLE_SUPERVISOR=true
+ENABLE_OUTPUT_SUPERVISOR=true
+ENABLE_PARALLEL_GUARDRAILS=true
+GUARDRAILS_FAIL_FAST=true
+OUTPUT_SUPERVISOR_MAX_RETRIES=3
+GUARDRAILS_CONFIG_PATH=./config/guardrails.yaml
+JUDGES_CONFIG_PATH=./config/judges.yaml
+PROMPT_POLICY_PATH=./config/prompt_policy.yaml
+
+###############################################################################
+# Gateway de canais
+###############################################################################
+DEFAULT_CHANNEL=web
+ENABLE_VOICE_ADAPTER=true
+ENABLE_WHATSAPP_ADAPTER=true
+ENABLE_TEXT_ADAPTER=true
+
+#################################################
+# ENTERPRISE ROUTING
+#################################################
+# Arquivo YAML com intents, keywords, políticas de estado e fallback.
+ROUTING_CONFIG_PATH=./config/routing.yaml
+# true = usa LLM para classificar quando keywords/estado não resolverem.
+# Em produção, costuma ser útil; em desenvolvimento, false evita custo e latência.
+ENABLE_LLM_ROUTER=true
+
+# Continuidade semântica, handoff humano e encerramento global.
+ENABLE_ROUTE_STICKINESS=true
+ROUTE_STICKINESS_LLM_PROFILE=route_continuity
+ROUTE_STICKINESS_CONFIDENCE_THRESHOLD=0.90
+ROUTE_STICKINESS_HISTORY_TURNS=2
+ROUTE_STICKINESS_MAX_TOKENS=80
+HUMAN_HANDOFF_MESSAGE=Vou encaminhar seu atendimento para uma pessoa.
+END_SESSION_MESSAGE=Atendimento encerrado. Obrigado pelo contato.
+SESSION_ALREADY_ENDED_MESSAGE=Este atendimento já foi encerrado. Inicie uma nova sessão para continuar.
+
+###############################################################################
+# MCP / Tools
+###############################################################################
+ENABLE_MCP_TOOLS=true
+MCP_SERVERS_CONFIG_PATH=./config/mcp_servers.yaml
+TOOLS_CONFIG_PATH=./config/tools.yaml
+TOOL_POLICIES_PATH=./config/tool_policies.yaml
+MCP_TOOL_TIMEOUT_SECONDS=30
+
+# router = EnterpriseRouter seleciona um agente; supervisor = pode acionar múltiplos agentes
+ROUTING_MODE=router
+
+# Usage/cost accounting
+USAGE_REPOSITORY_PROVIDER=sqlite
+IDENTITY_CONFIG_PATH=./config/identity.yaml
+MCP_PARAMETER_MAPPING_PATH=./config/mcp_parameter_mapping.yaml
+
+# -----------------------------------------------------------------------------
+# ConversationSummaryMemory / compressão de contexto conversacional
+# -----------------------------------------------------------------------------
+ENABLE_CONVERSATION_SUMMARY_MEMORY=true
+MEMORY_CONTEXT_STRATEGY=summary
+MEMORY_HISTORY_LIMIT=80
+MEMORY_RECENT_MESSAGES_LIMIT=8
+MEMORY_SUMMARY_TRIGGER_MESSAGES=20
+MEMORY_MAX_SUMMARY_CHARS=6000
+MEMORY_SUMMARY_USE_LLM=true
+MEMORY_INJECT_RECENT_MESSAGES=true
+MEMORY_INJECT_SUMMARY=true
+
+###############################################################################
+# MCP Gateway
+###############################################################################
+# true = framework routes tool calls to the dedicated MCP Gateway.
+# false = framework calls MCP servers directly from mcp_servers.yaml.
+MCP_GATEWAY_ENABLED=true
+MCP_GATEWAY_URL=http://localhost:8300
+MCP_GATEWAY_TIMEOUT_SECONDS=60
+# MCP_GATEWAY_TOKEN=
+MCP_GATEWAY_AGENT_ID=telecom_contas
+MCP_GATEWAY_TENANT_ID=default
+
+###############################################################################
+# LONG-TERM MEMORY
+###############################################################################
+ENABLE_LONG_TERM_MEMORY=true
+LONG_TERM_MEMORY_PROVIDER=sqlite
+LONG_TERM_MEMORY_SQLITE_PATH=./data/agent_framework.db
+LONG_TERM_MEMORY_TABLE=agentfw_long_term_memory
+# For Autonomous/Oracle, defaults to ${ADB_TABLE_PREFIX}_LONG_TERM_MEMORY
+# LONG_TERM_MEMORY_ORACLE_TABLE=AGENTFW_LONG_TERM_MEMORY
+LONG_TERM_MEMORY_MAX_CONTEXT_ITEMS=20
+LONG_TERM_MEMORY_MIN_CONFIDENCE=0.70
+LONG_TERM_MEMORY_AUTO_EXTRACT=true
+LONG_TERM_MEMORY_INJECT_CONTEXT=true

Tuning-Performance/Route_Stickness/templates/agent_template_backend_day_zero/config/mcp_parameter_mapping.yaml

@@ -60,6 +60,18 @@ mcp_parameter_mapping:
             mensagem.
           pattern: (?i)\\b(?:pedido|order)\\s*[:#-]?\\s*([A-Z0-9-]+)\\b
           group: 1
+    cancelar_pedido:
+      map:
+        session_key: session_id
+      extract:
+        order_id:
+          from: message
+          type: string
+          strategy: hybrid
+          description: Extraia somente o identificador do pedido informado explicitamente pelo usuário. Retorne null quando não houver identificador de pedido na mensagem.
+          pattern: (?i)\\b(?:pedido|order)\\s*[:#-]?\\s*([A-Z0-9-]+)\\b
+          group: 1
+
     solicitar_troca:
       map:
         session_key: session_id

Tuning-Performance/Route_Stickness/templates/agent_template_backend_day_zero/config/routing.yaml

@@ -79,6 +79,25 @@ intents:
       - Quero saber sobre meu pacote de internet.
       - Tenho roaming internacional?
 
+
+  - name: retail_order_cancel
+    domain: retail
+    agent: orders_agent
+    description: Cancelamento explícito de pedido ou compra.
+    priority: 20
+    mcp_tools:
+      - consultar_pedido
+      - cancelar_pedido
+    keywords:
+      - cancelar pedido
+      - cancelamento do pedido
+      - cancelar a compra
+      - cancelar compra
+    examples:
+      - Quero cancelar meu pedido.
+      - Cancele o pedido.
+      - Quero cancelar a compra.
+
   - name: retail_order_tracking
     domain: retail
     agent: orders_agent

Tuning-Performance/Route_Stickness/templates/agent_template_backend_day_zero/config/tool_policies.yaml

@@ -7,6 +7,12 @@ defaults:
   require_confirmation: false
 
 tool_policies:
+  cancelar_pedido:
+    operation_type: transactional
+    require_confirmation: true
+    requires: [order_id]
+
+
   solicitar_troca:
     operation_type: transactional
     require_confirmation: true

Tuning-Performance/Route_Stickness/templates/agent_template_backend_day_zero/config/tools.yaml

@@ -61,6 +61,23 @@ tools:
     - rastreamento
     - transportadora
     - previsão
+  cancelar_pedido:
+    description: Simula o cancelamento de um pedido de varejo.
+    mcp_server: retail
+    enabled: true
+    tool_type: action
+    requires:
+    - order_id
+    confirmation_required: true
+    args_schema:
+      order_id: string
+    selection_keywords:
+    - cancelar pedido
+    - cancelamento do pedido
+    - cancelar compra
+    - cancelar a compra
+
+
   solicitar_troca:
     description: Simula abertura de solicitação de troca.
     mcp_server: retail

Tuning-Performance/Transaction_Evidence/README.md

@@ -0,0 +1,172 @@
+# Transaction Evidence / Evidência Operacional de Transação
+
+Esta variante demonstra a funcionalidade **Transaction Evidence** do `agent_framework_oci`.
+Ela preserva o resultado estruturado de transações executadas no estado/checkpoint do
+LangGraph e o reutiliza, quando relevante, em turnos posteriores como evidência
+operacional para composição de resposta e groundedness.
+
+> Transaction Evidence **não é Long Term Memory**. É estado operacional associado à
+> sessão/workflow e aos recursos envolvidos na transação.
+
+## Problema que resolve
+
+Sem esta funcionalidade, uma transação pode ocorrer corretamente em um turno:
+
+```text
+quero cancelar pedido
+PED-1001
+sim
+→ protocolo CANCEL-2026-001
+```
+
+mas, no turno seguinte:
+
+```text
+quero meu pedido
+```
+
+o runtime pode consultar apenas `consultar_pedido`. Se a resposta mencionar o
+cancelamento/protocolo visto no turno anterior, o judge de groundedness pode tratar
+isso como informação não evidenciada.
+
+Com Transaction Evidence, o resultado transacional confirmado fica estruturado em
+`transaction_evidence` e pode aparecer como `relevant_transaction_evidence` quando
+correlacionado ao recurso atual.
+
+## Fluxo demonstrado
+
+```text
+cancelar_pedido(PED-1001)
+        ↓
+transação executada
+        ↓
+transaction_evidence
+  - tool_name
+  - arguments
+  - transaction_id
+  - status
+  - result/protocolo
+        ↓
+checkpoint da sessão
+        ↓
+novo turno: "quero meu pedido"
+        ↓
+consultar_pedido(PED-1001)
+        +
+relevant_transaction_evidence(PED-1001)
+        ↓
+resposta + groundedness com evidência operacional
+```
+
+## Regras de segurança e correlação
+
+A implementação do framework:
+
+- registra apenas resultados transacionais efetivamente executados;
+- mantém histórico bounded das evidências mais recentes;
+- correlaciona por identificadores de recurso, como `order_id`, `invoice_id`,
+  `asset_id`, `resource_key` e outros campos `*_id`;
+- evita reutilizar evidência de um recurso diferente;
+- disponibiliza somente a evidência relevante do turno em
+  `relevant_transaction_evidence`;
+- expõe a evidência relevante no metadata da resposta para diagnóstico;
+- inclui a evidência relevante no contexto usado pelos judges/groundedness.
+
+## Configuração
+
+Não existe uma variável obrigatória `ENABLE_TRANSACTION_EVIDENCE` nesta versão.
+A funcionalidade faz parte do runtime transacional do framework e depende da
+persistência de estado/checkpoint já usada pelo agente.
+
+Os principais pontos do exemplo são:
+
+- `app/state.py`: campos `transaction_evidence`, `last_transaction_evidence` e
+  `relevant_transaction_evidence`;
+- `app/workflows/agent_graph.py`: injeta evidência relevante no contexto dos judges;
+- `app/main.py`: publica `transaction_evidence` no metadata da resposta;
+- framework: `runtime/agent_runtime.py`: grava, correlaciona e materializa a evidência.
+
+## Executar
+
+```bash
+cd Tuning-Performance/Transaction_Evidence/agent_template_backend
+pip install -e ../../../libs/agent_framework
+pip install -r requirements.txt
+python -m uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload
+```
+
+Use o frontend normalmente apontando para `http://localhost:8000`.
+
+## Cenário de teste recomendado
+
+Execute na mesma sessão:
+
+```text
+quero meu pedido
+quero cancelar pedido
+PED-1001
+sim
+quero meu pedido
+```
+
+Na última rodada, valide no JSON de resposta:
+
+```json
+{
+  "route": "orders_agent",
+  "intent": "retail_order_tracking",
+  "transaction_evidence": [
+    {
+      "tool_name": "cancelar_pedido",
+      "status": "COMPLETED"
+    }
+  ]
+}
+```
+
+A estrutura exata do `result` depende da tool transacional, mas o protocolo retornado
+pela transação deve estar presente na evidência relevante quando o recurso correlacionar.
+
+## Resultado esperado
+
+No último `quero meu pedido`:
+
+1. a intent deve ser `retail_order_tracking`;
+2. `consultar_pedido` deve ser executada normalmente;
+3. a evidência da transação anterior de `PED-1001` deve ser recuperada;
+4. a resposta pode mencionar o cancelamento/protocolo porque essa informação agora
+   está suportada por evidência operacional;
+5. o judge de groundedness deve receber essa mesma evidência.
+
+## Diferença para Long Term Memory
+
+| Transaction Evidence | Long Term Memory |
+|---|---|
+| Estado operacional | Memória semântica/pessoal |
+| Resultado de tools/transações | Preferências e fatos duráveis |
+| Correlacionado a recursos | Correlacionado ao sujeito |
+| Usado para grounding operacional | Usado para personalização/contexto |
+| Curto/médio prazo de workflow | Longo prazo entre conversas |
+
+---
+
+## English summary
+
+This variant demonstrates **Transaction Evidence**: executed transactional tool
+outcomes are persisted as structured operational evidence in LangGraph state/checkpoint.
+On later turns, the runtime correlates prior evidence with the current resource and
+exposes it as `relevant_transaction_evidence` to both response composition and
+judges/groundedness. It is operational workflow state, not Long Term Memory.
+
+Recommended test sequence:
+
+```text
+show my order
+cancel my order
+PED-1001
+yes
+show my order
+```
+
+On the last turn, verify that the response metadata contains relevant transaction
+evidence for the cancellation of `PED-1001`.

Tuning-Performance/Transaction_Evidence/agent_template_backend/.env

@@ -0,0 +1,207 @@
+###############################################################################
+# AI AGENT PLATFORM - CONFIGURAÇÃO ÚNICA
+# Este arquivo é lido por Pydantic Settings no framework e no backend template.
+###############################################################################
+
+APP_NAME=ai-agent-template
+APP_ENV=local
+LOG_LEVEL=INFO
+API_HOST=0.0.0.0
+API_PORT=8000
+CORS_ORIGINS=http://localhost:5173,http://127.0.0.1:5173
+
+###############################################################################
+# LLM - OCI Generative AI como provider principal
+###############################################################################
+# Opções: mock, oci_openai, oci_sdk, openai_compatible
+LLM_PROVIDER=oci_sdk
+LLM_TEMPERATURE=0.2
+LLM_MAX_TOKENS=2048
+LLM_TIMEOUT_SECONDS=120
+
+# OCI OpenAI-compatible endpoint
+OCI_GENAI_BASE_URL=https://inference.generativeai.us-chicago-1.oci.oraclecloud.com
+OCI_GENAI_MODEL=openai.gpt-4.1
+OCI_GENAI_API_KEY=sk-ph3FgX6iP3fxAQCXb9IpPIDTadkeeYAWntUWhzcWysIM6zsS
+OCI_GENAI_PROJECT_OCID=
+
+#OCI_GENAI_BASE_URL=https://pegruagntaiatenddev.pe.inference.generativeai.sa-saopaulo-1.oci.oraclecloud.com
+#OCI_GENAI_MODEL=openai.gpt-4.1
+#OCI_GENAI_API_KEY=
+#OCI_GENAI_PROJECT_OCID=
+
+
+# OCI_AUTH_MODE=config_file|instance_principal|resource_principal
+OCI_AUTH_MODE=config_file
+# OCI SDK / signer / profiles
+OCI_CONFIG_FILE=~/.oci/config
+OCI_PROFILE=LATINOAMERICA-Chicago
+OCI_COMPARTMENT_ID=ocid1.compartment.oc1..aaaaaaaaexpiw4a7dio64mkfv2t273s2hgdl6mgfvvyv7tycalnjlvpvfl3q
+OCI_REGION=us-chicago-1
+
+###############################################################################
+# Persistência
+###############################################################################
+# Opções: memory, autonomous, mongodb
+SESSION_REPOSITORY_PROVIDER=autonomous
+MEMORY_REPOSITORY_PROVIDER=autonomous
+CHECKPOINT_REPOSITORY_PROVIDER=autonomous
+
+# Autonomous Database
+ADB_USER=admin
+ADB_PASSWORD=Moniquinha19721972
+ADB_DSN=oradb23ai_high
+ADB_WALLET_LOCATION=/mnt/d/Dropbox/ORACLE/LatinoAmerica/Wallet_ORADB23ai
+ADB_WALLET_PASSWORD=Moniquinha1972
+ADB_TABLE_PREFIX=AGENTFW
+
+# MongoDB - também pode representar Autonomous usando API compatível com Mongo, se habilitada no ambiente
+MONGODB_URI=mongodb://mongo:mongopassword@localhost:27017
+MONGODB_DATABASE=agent_platform
+
+# Redis
+REDIS_URL=redis://localhost:6379/0
+ENABLE_REDIS_CACHE=false
+
+###############################################################################
+# RAG / Vector / Graph
+###############################################################################
+VECTOR_STORE_PROVIDER=autonomous
+GRAPH_STORE_PROVIDER=autonomous
+RAG_TOP_K=5
+EMBEDDING_PROVIDER=oci
+OCI_EMBEDDING_MODEL=cohere.embed-multilingual-v3.0
+RAG_FILE_GLOBS=*.md,*.txt,*.yaml,*.yml,*.json
+
+###############################################################################
+# Observabilidade
+###############################################################################
+ENABLE_LANGFUSE=true
+ # Opcional: verbose, compact
+LANGFUSE_TRACE_MODE=compact
+# Nome customizado do trace pai, ex.: backoffice.checklist.workflow ou backoffice.emulador.workflow
+LANGFUSE_COMPACT_VISIBLE_EVENT_PREFIXES=AGA.,NOC., IC.
+LANGFUSE_COMPACT_SUPPRESSED_PREFIXES=llm.chat_completion
+LANGFUSE_IGNORE_HEALTHCHECKS=true
+LANGFUSE_IGNORED_PATHS=/health,/ready,/metrics
+LANGFUSE_PUBLIC_KEY=pk-lf-4a1e3921-5158-4fd3-a16d-7a77549fb312
+LANGFUSE_SECRET_KEY=sk-lf-efc6fd59-c5ec-4858-b6ec-4aa129734915
+LANGFUSE_HOST=http://localhost:3005
+ENABLE_OTEL=false
+OTEL_EXPORTER_OTLP_ENDPOINT=
+OTEL_SERVICE_NAME=ai-agent-template
+ENABLE_LANGFUSE_OPENAI_AUTO_INSTRUMENTATION=true
+ENABLE_LANGFUSE_ANALYTICS_PUBLISHER=false
+
+###############################################################################
+# Analytics / Observer corporativo
+###############################################################################
+# Quando true, AgentObserver publica eventos IC.*, NOC.* e GRL.* nos providers abaixo.
+ENABLE_ANALYTICS=false
+# Providers aceitos: oci_streaming,pubsub,noop
+ANALYTICS_PROVIDERS=oci_streaming
+# Compatibilidade FIRST/TIM: pode informar AGENT_PUBSUB_TOPIC diretamente.
+AGENT_PUBSUB_TOPIC=
+GCP_PUBSUB_TOPIC_PATH=
+GCP_PROJECT_ID=
+GCP_PUBSUB_TOPIC=
+GCP_PUBSUB_TIMEOUT_SECONDS=30
+# Credencial GCP segue padrão Google:
+# GOOGLE_APPLICATION_CREDENTIALS=/secrets/gcp-service-account.json
+
+###############################################################################
+# OCI Streaming
+###############################################################################
+ENABLE_OCI_STREAMING=false
+OCI_STREAM_ENDPOINT=
+OCI_STREAM_OCID=
+OCI_STREAM_PARTITION_KEY=agent-events
+
+###############################################################################
+# Guardrails, Judges, Supervisor
+###############################################################################
+ENABLE_INPUT_GUARDRAILS=true
+ENABLE_OUTPUT_GUARDRAILS=true
+ENABLE_JUDGES=true
+ENABLE_SUPERVISOR=true
+ENABLE_OUTPUT_SUPERVISOR=true
+ENABLE_PARALLEL_GUARDRAILS=true
+GUARDRAILS_FAIL_FAST=true
+OUTPUT_SUPERVISOR_MAX_RETRIES=3
+GUARDRAILS_CONFIG_PATH=./config/guardrails.yaml
+JUDGES_CONFIG_PATH=./config/judges.yaml
+PROMPT_POLICY_PATH=./config/prompt_policy.yaml
+
+###############################################################################
+# Gateway de canais
+###############################################################################
+DEFAULT_CHANNEL=web
+# embedded = backend may parse simple/native channel payloads.
+# external = backend only accepts GatewayRequest normalized by an external Channel Gateway.
+FRAMEWORK_CHANNEL_INPUT_MODE=embedded
+ENABLE_VOICE_ADAPTER=true
+ENABLE_WHATSAPP_ADAPTER=true
+ENABLE_TEXT_ADAPTER=true
+
+#################################################
+# ENTERPRISE ROUTING
+#################################################
+# Arquivo YAML com intents, keywords, políticas de estado e fallback.
+ROUTING_CONFIG_PATH=./config/routing.yaml
+# true = usa LLM para classificar quando keywords/estado não resolverem.
+# Em produção, costuma ser útil; em desenvolvimento, false evita custo e latência.
+ENABLE_LLM_ROUTER=true
+
+# Semantic route stickiness (optional).
+# Uses a lightweight LLM profile to decide only CONTINUE vs ROUTE.
+# There are no regexes or deterministic language rules.
+ENABLE_ROUTE_STICKINESS=true
+ROUTE_STICKINESS_LLM_PROFILE=route_continuity
+ROUTE_STICKINESS_CONFIDENCE_THRESHOLD=0.90
+ROUTE_STICKINESS_HISTORY_TURNS=2
+ROUTE_STICKINESS_MAX_TOKENS=80
+HUMAN_HANDOFF_MESSAGE=Vou encaminhar seu atendimento para uma pessoa.
+END_SESSION_MESSAGE=Atendimento encerrado. Obrigado pelo contato.
+
+###############################################################################
+# MCP / Tools
+###############################################################################
+ENABLE_MCP_TOOLS=true
+MCP_SERVERS_CONFIG_PATH=./config/mcp_servers.yaml
+TOOLS_CONFIG_PATH=./config/tools.yaml
+MCP_TOOL_TIMEOUT_SECONDS=30
+
+# router = EnterpriseRouter seleciona um agente; supervisor = pode acionar múltiplos agentes
+ROUTING_MODE=router
+
+# Usage/cost accounting
+USAGE_REPOSITORY_PROVIDER=autonomous
+IDENTITY_CONFIG_PATH=./config/identity.yaml
+MCP_PARAMETER_MAPPING_PATH=./config/mcp_parameter_mapping.yaml
+
+# -----------------------------------------------------------------------------
+# ConversationSummaryMemory / compressão de contexto conversacional
+# -----------------------------------------------------------------------------
+ENABLE_CONVERSATION_SUMMARY_MEMORY=true
+MEMORY_CONTEXT_STRATEGY=summary
+MEMORY_HISTORY_LIMIT=80
+MEMORY_RECENT_MESSAGES_LIMIT=8
+MEMORY_SUMMARY_TRIGGER_MESSAGES=20
+MEMORY_MAX_SUMMARY_CHARS=6000
+MEMORY_SUMMARY_USE_LLM=true
+MEMORY_INJECT_RECENT_MESSAGES=true
+MEMORY_INJECT_SUMMARY=true
+
+###############################################################################
+# LONG-TERM MEMORY
+###############################################################################
+ENABLE_LONG_TERM_MEMORY=true
+LONG_TERM_MEMORY_PROVIDER=sqlite
+LONG_TERM_MEMORY_SQLITE_PATH=./data/agent_framework.db
+LONG_TERM_MEMORY_TABLE=agentfw_long_term_memory
+# For Autonomous/Oracle, defaults to ${ADB_TABLE_PREFIX}_LONG_TERM_MEMORY
+# LONG_TERM_MEMORY_ORACLE_TABLE=AGENTFW_LONG_TERM_MEMORY
+LONG_TERM_MEMORY_MAX_CONTEXT_ITEMS=20
+LONG_TERM_MEMORY_MIN_CONFIDENCE=0.70
+LONG_TERM_MEMORY_AUTO_EXTRACT=true
+LONG_TERM_MEMORY_INJECT_CONTEXT=true

Tuning-Performance/Transaction_Evidence/agent_template_backend/Dockerfile

@@ -0,0 +1,6 @@
+FROM python:3.12-slim
+WORKDIR /app
+COPY agent_framework /agent_framework
+COPY agent_template_backend /app
+RUN pip install --no-cache-dir -e /agent_framework -r requirements.txt
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]

Tuning-Performance/Transaction_Evidence/agent_template_backend/README_ENTERPRISE_TEMPLATE.md

@@ -0,0 +1,54 @@
+# Agent Template Backend Enterprise
+
+Este folder é uma cópia completa do `agent_template_backend`, sem cortes de
+arquitetura. Ele mantém workflow, router, output supervisor, guardrails,
+analytics, observer, MCP, memória, checkpoints e configurações.
+
+A diferença é que a lógica de negócio dos agentes de exemplo foi removida da
+execução e preservada comentada nos próprios arquivos:
+
+- `app/agents/billing_agent.py`
+- `app/agents/product_agent.py`
+- `app/agents/orders_agent.py`
+- `app/agents/support_agent.py`
+
+## O que o desenvolvedor deve alterar
+
+1. Escolher ou criar um agente em `app/agents/`.
+2. Implementar o método `run()`.
+3. Ajustar prompts e tools, se necessário.
+4. Emitir ICs de negócio relevantes para a jornada.
+5. Manter NOC/GRL nos pontos operacionais e de guardrails.
+
+## O que já está integrado
+
+- `AgentObserver`
+- `observer.emit_ic()`
+- `observer.emit_noc()`
+- `observer.emit_grl()`
+- `AnalyticsPublisher`
+- OCI Streaming
+- GCP Pub/Sub
+- OutputSupervisor
+- GuardrailPipeline com suporte a execução paralela/fail-fast no framework
+- MCP Tool Router
+- LangGraph
+- Memory
+- Checkpoint
+- Langfuse / OpenTelemetry
+
+## Exemplos adicionados
+
+Veja `app/examples/`:
+
+- `ic_examples.py`
+- `noc_examples.py`
+- `grl_examples.py`
+- `mcp_examples.py`
+- `observer_examples.py`
+
+## Convenção rápida
+
+- IC = evento de negócio / curadoria / informacional.
+- NOC = evento operacional / saúde técnica.
+- GRL = evento de guardrail / segurança / validação.

Tuning-Performance/Transaction_Evidence/agent_template_backend/app/agents/README.md

@@ -0,0 +1,15 @@
+# Agentes do Template Backend Enterprise
+
+Os arquivos desta pasta preservam a estrutura real esperada pelo workflow, mas
+não executam lógica de negócio pronta.
+
+Cada agente mostra:
+
+- como emitir IC;
+- como emitir NOC;
+- como emitir GRL;
+- como coletar MCP via `_collect_tool_context()`;
+- como recuperar RAG via `_retrieve_rag_context()`;
+- onde chamar LLM/cache.
+
+A implementação original do exemplo está comentada no fim de cada arquivo.

Tuning-Performance/Transaction_Evidence/agent_template_backend/app/agents/billing_agent.py

@@ -0,0 +1,129 @@
+from app.agents.prompting import apply_agent_profile_prompt
+from app.agents.runtime import AgentRuntimeMixin
+
+
+class BillingAgent(AgentRuntimeMixin):
+    name = "billingAgent"
+
+    def __init__(
+        self,
+        llm,
+        telemetry=None,
+        tool_router=None,
+        rag_service=None,
+        cache=None,
+        settings=None,
+        observer=None,
+        memory=None,
+        summary_memory=None,
+    ):
+        self.llm = llm
+        self.telemetry = telemetry
+        self.tool_router = tool_router
+        self.rag_service = rag_service
+        self.cache = cache
+        self.settings = settings
+        self.observer = observer
+        self.memory = memory
+        self.summary_memory = summary_memory
+
+    async def run(self, state):
+        await self._emit_ic(
+            "IC.BILLING_AGENT_STARTED",
+            state,
+            {"business_component": "faturas"},
+            component="agent.billing.start",
+        )
+
+        tool_context = await self._collect_tool_context(state)
+        if tool_context:
+            await self._emit_ic(
+                "IC.BILLING_MCP_CONTEXT_COLLECTED",
+                state,
+                {"tool_result_count": len(tool_context)},
+                component="agent.billing.mcp",
+            )
+
+        state["mcp_results"] = tool_context
+        clarification_message = self.transaction_clarification_message(state)
+        if clarification_message:
+            return {
+                "answer": f"[{self.__class__.__name__}] {clarification_message}",
+                "next_state": state.get("next_state") or "COLLECTING_PARAMETERS",
+                "mcp_results": tool_context,
+                **self.transaction_state_patch(state),
+            }
+
+        confirmation_message = self.transaction_confirmation_message(state)
+        if confirmation_message:
+            result = {
+                "answer": f"[{self.__class__.__name__}] {confirmation_message}",
+                "next_state": state.get("next_state"),
+                "mcp_results": tool_context,
+                **self.transaction_state_patch(state),
+            }
+            return result
+
+        direct_answer = self.build_direct_mcp_answer(state, tool_context, agent_label="BillingAgent")
+        if direct_answer:
+            return {
+                "answer": direct_answer,
+                "next_state": state.get("next_state") or "ACTIVE",
+                "mcp_results": tool_context,
+                "rag": {"enabled": False, "skipped": True, "reason": "direct_mcp_answer"},
+                **self.transaction_state_patch(state),
+            }
+
+        rag_context, rag_metadata = await self._retrieve_rag_context(state)
+        if rag_metadata.get("enabled"):
+            await self._emit_ic(
+                "IC.BILLING_RAG_CONTEXT_RETRIEVED",
+                state,
+                {
+                    "document_count": rag_metadata.get("document_count"),
+                    "graph_neighbors": rag_metadata.get("graph_neighbors"),
+                    "latency_ms": rag_metadata.get("latency_ms"),
+                },
+                component="agent.billing.rag",
+            )
+
+        # Prepara ConversationSummaryMemory antes de montar o prompt.
+        # O build_messages() do framework injeta resumo + últimas mensagens quando habilitado.
+        await self.prepare_memory_context(state)
+
+        messages = self.build_messages(
+            state,
+            system_prompt=apply_agent_profile_prompt(
+                state,
+                "Você é um agente especialista em faturas. Responda com clareza, objetividade e sem sugerir ações não solicitadas. Use dados MCP quando disponíveis.",
+            ),
+            mcp_results=tool_context,
+            rag_context=rag_context,
+            rag_metadata=rag_metadata,
+        )
+
+        answer = await self._invoke_llm_cached(state, "BillingAgent", messages)
+        result = {
+            "answer": f"[BillingAgent] {answer}",
+            "next_state": "BILLING_ACTIVE",
+            "mcp_results": tool_context,
+            "rag": rag_metadata,
+            "memory_context_metadata": state.get("memory_context_metadata"),
+            **self.transaction_state_patch(state),
+        }
+
+        await self._emit_ic(
+            "IC.BILLING_AGENT_COMPLETED",
+            state,
+            {
+                "answer_chars": len(result.get("answer") or ""),
+                "has_mcp_results": bool(tool_context),
+                "rag_enabled": bool(rag_metadata.get("enabled")),
+                "memory_context": state.get("memory_context_metadata"),
+            },
+            component="agent.billing.completed",
+        )
+        return result
+
+    async def _collect_tool_context(self, state):
+        return await self._collect_mcp_context(state)

Tuning-Performance/Transaction_Evidence/agent_template_backend/app/agents/orders_agent.py

@@ -0,0 +1,129 @@
+from app.agents.prompting import apply_agent_profile_prompt
+from app.agents.runtime import AgentRuntimeMixin
+
+
+class OrdersAgent(AgentRuntimeMixin):
+    name = "orders_agent"
+
+    def __init__(
+        self,
+        llm,
+        telemetry=None,
+        tool_router=None,
+        rag_service=None,
+        cache=None,
+        settings=None,
+        observer=None,
+        memory=None,
+        summary_memory=None,
+    ):
+        self.llm = llm
+        self.telemetry = telemetry
+        self.tool_router = tool_router
+        self.rag_service = rag_service
+        self.cache = cache
+        self.settings = settings
+        self.observer = observer
+        self.memory = memory
+        self.summary_memory = summary_memory
+
+    async def run(self, state):
+        await self._emit_ic(
+            "IC.ORDERS_AGENT_STARTED",
+            state,
+            {"business_component": "pedidos"},
+            component="agent.orders.start",
+        )
+
+        tool_context = await self._collect_tool_context(state)
+        if tool_context:
+            await self._emit_ic(
+                "IC.ORDERS_MCP_CONTEXT_COLLECTED",
+                state,
+                {"tool_result_count": len(tool_context)},
+                component="agent.orders.mcp",
+            )
+
+        state["mcp_results"] = tool_context
+        clarification_message = self.transaction_clarification_message(state)
+        if clarification_message:
+            return {
+                "answer": f"[{self.__class__.__name__}] {clarification_message}",
+                "next_state": state.get("next_state") or "COLLECTING_PARAMETERS",
+                "mcp_results": tool_context,
+                **self.transaction_state_patch(state),
+            }
+
+        confirmation_message = self.transaction_confirmation_message(state)
+        if confirmation_message:
+            result = {
+                "answer": f"[{self.__class__.__name__}] {confirmation_message}",
+                "next_state": state.get("next_state"),
+                "mcp_results": tool_context,
+                **self.transaction_state_patch(state),
+            }
+            return result
+
+        direct_answer = self.build_direct_mcp_answer(state, tool_context, agent_label="OrdersAgent")
+        if direct_answer:
+            return {
+                "answer": direct_answer,
+                "next_state": state.get("next_state") or "ACTIVE",
+                "mcp_results": tool_context,
+                "rag": {"enabled": False, "skipped": True, "reason": "direct_mcp_answer"},
+                **self.transaction_state_patch(state),
+            }
+
+        rag_context, rag_metadata = await self._retrieve_rag_context(state)
+        if rag_metadata.get("enabled"):
+            await self._emit_ic(
+                "IC.ORDERS_RAG_CONTEXT_RETRIEVED",
+                state,
+                {
+                    "document_count": rag_metadata.get("document_count"),
+                    "graph_neighbors": rag_metadata.get("graph_neighbors"),
+                    "latency_ms": rag_metadata.get("latency_ms"),
+                },
+                component="agent.orders.rag",
+            )
+
+        # Prepara ConversationSummaryMemory antes de montar o prompt.
+        # O build_messages() do framework injeta resumo + últimas mensagens quando habilitado.
+        await self.prepare_memory_context(state)
+
+        messages = self.build_messages(
+            state,
+            system_prompt=apply_agent_profile_prompt(
+                state,
+                "Você é um agente de pedidos de varejo. Use dados de tools quando disponíveis.",
+            ),
+            mcp_results=tool_context,
+            rag_context=rag_context,
+            rag_metadata=rag_metadata,
+        )
+
+        answer = await self._invoke_llm_cached(state, "OrdersAgent", messages)
+        result = {
+            "answer": f"[OrdersAgent] {answer}",
+            "next_state": "ORDER_ACTIVE",
+            "mcp_results": tool_context,
+            "rag": rag_metadata,
+            "memory_context_metadata": state.get("memory_context_metadata"),
+            **self.transaction_state_patch(state),
+        }
+
+        await self._emit_ic(
+            "IC.ORDERS_AGENT_COMPLETED",
+            state,
+            {
+                "answer_chars": len(result.get("answer") or ""),
+                "has_mcp_results": bool(tool_context),
+                "rag_enabled": bool(rag_metadata.get("enabled")),
+                "memory_context": state.get("memory_context_metadata"),
+            },
+            component="agent.orders.completed",
+        )
+        return result
+
+    async def _collect_tool_context(self, state):
+        return await self._collect_mcp_context(state)

Tuning-Performance/Transaction_Evidence/agent_template_backend/app/agents/product_agent.py

@@ -0,0 +1,129 @@
+from app.agents.prompting import apply_agent_profile_prompt
+from app.agents.runtime import AgentRuntimeMixin
+
+
+class ProductAgent(AgentRuntimeMixin):
+    name = "productAgent"
+
+    def __init__(
+        self,
+        llm,
+        telemetry=None,
+        tool_router=None,
+        rag_service=None,
+        cache=None,
+        settings=None,
+        observer=None,
+        memory=None,
+        summary_memory=None,
+    ):
+        self.llm = llm
+        self.telemetry = telemetry
+        self.tool_router = tool_router
+        self.rag_service = rag_service
+        self.cache = cache
+        self.settings = settings
+        self.observer = observer
+        self.memory = memory
+        self.summary_memory = summary_memory
+
+    async def run(self, state):
+        await self._emit_ic(
+            "IC.PRODUCT_AGENT_STARTED",
+            state,
+            {"business_component": "produtos"},
+            component="agent.product.start",
+        )
+
+        tool_context = await self._collect_tool_context(state)
+        if tool_context:
+            await self._emit_ic(
+                "IC.PRODUCT_MCP_CONTEXT_COLLECTED",
+                state,
+                {"tool_result_count": len(tool_context)},
+                component="agent.product.mcp",
+            )
+
+        state["mcp_results"] = tool_context
+        clarification_message = self.transaction_clarification_message(state)
+        if clarification_message:
+            return {
+                "answer": f"[{self.__class__.__name__}] {clarification_message}",
+                "next_state": state.get("next_state") or "COLLECTING_PARAMETERS",
+                "mcp_results": tool_context,
+                **self.transaction_state_patch(state),
+            }
+
+        confirmation_message = self.transaction_confirmation_message(state)
+        if confirmation_message:
+            result = {
+                "answer": f"[{self.__class__.__name__}] {confirmation_message}",
+                "next_state": state.get("next_state"),
+                "mcp_results": tool_context,
+                **self.transaction_state_patch(state),
+            }
+            return result
+
+        direct_answer = self.build_direct_mcp_answer(state, tool_context, agent_label="ProductAgent")
+        if direct_answer:
+            return {
+                "answer": direct_answer,
+                "next_state": state.get("next_state") or "ACTIVE",
+                "mcp_results": tool_context,
+                "rag": {"enabled": False, "skipped": True, "reason": "direct_mcp_answer"},
+                **self.transaction_state_patch(state),
+            }
+
+        rag_context, rag_metadata = await self._retrieve_rag_context(state)
+        if rag_metadata.get("enabled"):
+            await self._emit_ic(
+                "IC.PRODUCT_RAG_CONTEXT_RETRIEVED",
+                state,
+                {
+                    "document_count": rag_metadata.get("document_count"),
+                    "graph_neighbors": rag_metadata.get("graph_neighbors"),
+                    "latency_ms": rag_metadata.get("latency_ms"),
+                },
+                component="agent.product.rag",
+            )
+
+        # Prepara ConversationSummaryMemory antes de montar o prompt.
+        # O build_messages() do framework injeta resumo + últimas mensagens quando habilitado.
+        await self.prepare_memory_context(state)
+
+        messages = self.build_messages(
+            state,
+            system_prompt=apply_agent_profile_prompt(
+                state,
+                "Você é um agente especialista em produtos, planos e serviços. Explique sem fazer oferta proativa e sem executar ações sem confirmação. Use dados MCP quando disponíveis.",
+            ),
+            mcp_results=tool_context,
+            rag_context=rag_context,
+            rag_metadata=rag_metadata,
+        )
+
+        answer = await self._invoke_llm_cached(state, "ProductAgent", messages)
+        result = {
+            "answer": f"[ProductAgent] {answer}",
+            "next_state": "PRODUCT_ACTIVE",
+            "mcp_results": tool_context,
+            "rag": rag_metadata,
+            "memory_context_metadata": state.get("memory_context_metadata"),
+            **self.transaction_state_patch(state),
+        }
+
+        await self._emit_ic(
+            "IC.PRODUCT_AGENT_COMPLETED",
+            state,
+            {
+                "answer_chars": len(result.get("answer") or ""),
+                "has_mcp_results": bool(tool_context),
+                "rag_enabled": bool(rag_metadata.get("enabled")),
+                "memory_context": state.get("memory_context_metadata"),
+            },
+            component="agent.product.completed",
+        )
+        return result
+
+    async def _collect_tool_context(self, state):
+        return await self._collect_mcp_context(state)

Tuning-Performance/Transaction_Evidence/agent_template_backend/app/agents/prompting.py

@@ -0,0 +1,15 @@
+from __future__ import annotations
+
+
+def apply_agent_profile_prompt(state: dict, default_prompt: str) -> str:
+    """Adiciona o prefixo de prompt configurado para o agent_template selecionado.
+
+    Cada agent_id pode definir metadata.system_prefix em config/agents.yaml. Isso
+    mantém prompts isolados sem duplicar o código dos agentes especializados.
+    """
+    profile = state.get("agent_profile") or (state.get("context") or {}).get("agent_profile") or {}
+    metadata = profile.get("metadata") or {}
+    prefix = (metadata.get("system_prefix") or "").strip()
+    if not prefix:
+        return default_prompt
+    return f"{prefix}\n\n{default_prompt}"

Tuning-Performance/Transaction_Evidence/agent_template_backend/app/agents/runtime.py

@@ -0,0 +1,10 @@
+from __future__ import annotations
+
+# Compatibilidade local do template/backend.
+# A implementação oficial agora fica no framework para evitar duplicação entre agentes.
+from agent_framework.runtime import AgentRuntimeMixin, MessageBuilder, RuntimeContext
+from app.presentation import register_tool_renderers
+
+register_tool_renderers()
+
+__all__ = ["AgentRuntimeMixin", "MessageBuilder", "RuntimeContext"]

Tuning-Performance/Transaction_Evidence/agent_template_backend/app/agents/support_agent.py

@@ -0,0 +1,129 @@
+from app.agents.prompting import apply_agent_profile_prompt
+from app.agents.runtime import AgentRuntimeMixin
+
+
+class SupportAgent(AgentRuntimeMixin):
+    name = "support_agent"
+
+    def __init__(
+        self,
+        llm,
+        telemetry=None,
+        tool_router=None,
+        rag_service=None,
+        cache=None,
+        settings=None,
+        observer=None,
+        memory=None,
+        summary_memory=None,
+    ):
+        self.llm = llm
+        self.telemetry = telemetry
+        self.tool_router = tool_router
+        self.rag_service = rag_service
+        self.cache = cache
+        self.settings = settings
+        self.observer = observer
+        self.memory = memory
+        self.summary_memory = summary_memory
+
+    async def run(self, state):
+        await self._emit_ic(
+            "IC.SUPPORT_AGENT_STARTED",
+            state,
+            {"business_component": "suporte"},
+            component="agent.support.start",
+        )
+
+        tool_context = await self._collect_tool_context(state)
+        if tool_context:
+            await self._emit_ic(
+                "IC.SUPPORT_MCP_CONTEXT_COLLECTED",
+                state,
+                {"tool_result_count": len(tool_context)},
+                component="agent.support.mcp",
+            )
+
+        state["mcp_results"] = tool_context
+        clarification_message = self.transaction_clarification_message(state)
+        if clarification_message:
+            return {
+                "answer": f"[{self.__class__.__name__}] {clarification_message}",
+                "next_state": state.get("next_state") or "COLLECTING_PARAMETERS",
+                "mcp_results": tool_context,
+                **self.transaction_state_patch(state),
+            }
+
+        confirmation_message = self.transaction_confirmation_message(state)
+        if confirmation_message:
+            result = {
+                "answer": f"[{self.__class__.__name__}] {confirmation_message}",
+                "next_state": state.get("next_state"),
+                "mcp_results": tool_context,
+                **self.transaction_state_patch(state),
+            }
+            return result
+
+        direct_answer = self.build_direct_mcp_answer(state, tool_context, agent_label="SupportAgent")
+        if direct_answer:
+            return {
+                "answer": direct_answer,
+                "next_state": state.get("next_state") or "ACTIVE",
+                "mcp_results": tool_context,
+                "rag": {"enabled": False, "skipped": True, "reason": "direct_mcp_answer"},
+                **self.transaction_state_patch(state),
+            }
+
+        rag_context, rag_metadata = await self._retrieve_rag_context(state)
+        if rag_metadata.get("enabled"):
+            await self._emit_ic(
+                "IC.SUPPORT_RAG_CONTEXT_RETRIEVED",
+                state,
+                {
+                    "document_count": rag_metadata.get("document_count"),
+                    "graph_neighbors": rag_metadata.get("graph_neighbors"),
+                    "latency_ms": rag_metadata.get("latency_ms"),
+                },
+                component="agent.support.rag",
+            )
+
+        # Prepara ConversationSummaryMemory antes de montar o prompt.
+        # O build_messages() do framework injeta resumo + últimas mensagens quando habilitado.
+        await self.prepare_memory_context(state)
+
+        messages = self.build_messages(
+            state,
+            system_prompt=apply_agent_profile_prompt(
+                state,
+                "Você é um agente de suporte de varejo para troca, devolução e garantia.",
+            ),
+            mcp_results=tool_context,
+            rag_context=rag_context,
+            rag_metadata=rag_metadata,
+        )
+
+        answer = await self._invoke_llm_cached(state, "SupportAgent", messages)
+        result = {
+            "answer": f"[SupportAgent] {answer}",
+            "next_state": "SUPPORT_ACTIVE",
+            "mcp_results": tool_context,
+            "rag": rag_metadata,
+            "memory_context_metadata": state.get("memory_context_metadata"),
+            **self.transaction_state_patch(state),
+        }
+
+        await self._emit_ic(
+            "IC.SUPPORT_AGENT_COMPLETED",
+            state,
+            {
+                "answer_chars": len(result.get("answer") or ""),
+                "has_mcp_results": bool(tool_context),
+                "rag_enabled": bool(rag_metadata.get("enabled")),
+                "memory_context": state.get("memory_context_metadata"),
+            },
+            component="agent.support.completed",
+        )
+        return result
+
+    async def _collect_tool_context(self, state):
+        return await self._collect_mcp_context(state)

Tuning-Performance/Transaction_Evidence/agent_template_backend/app/examples/__init__.py

@@ -0,0 +1 @@
+"""Exemplos de uso do template backend enterprise."""

Tuning-Performance/Transaction_Evidence/agent_template_backend/app/examples/grl_examples.py

@@ -0,0 +1,37 @@
+"""Exemplos de GRL.
+
+GRL representa eventos de guardrails. Em regra, GRL.001..GRL.009 são emitidos
+pelo pipeline de guardrails e pelo OutputSupervisor do framework. Use emissão
+manual apenas para validações customizadas do agente.
+"""
+
+from typing import Any
+
+
+async def exemplo_guardrail_observado(observer: Any, state: dict[str, Any], rail_code: str, reason: str) -> None:
+    await observer.emit_grl(
+        "OBSERVE",
+        {
+            "session_id": state.get("conversation_key") or state.get("session_id"),
+            "tenant_id": state.get("tenant_id"),
+            "agent_id": state.get("agent_id"),
+            "rail_code": rail_code,
+            "reason": reason,
+        },
+        component="examples.grl",
+    )
+
+
+async def exemplo_guardrail_block(observer: Any, state: dict[str, Any], rail_code: str, reason: str) -> None:
+    await observer.emit_grl(
+        "004",
+        {
+            "session_id": state.get("conversation_key") or state.get("session_id"),
+            "tenant_id": state.get("tenant_id"),
+            "agent_id": state.get("agent_id"),
+            "rail_code": rail_code,
+            "reason": reason,
+            "action": "block",
+        },
+        component="examples.grl",
+    )

Tuning-Performance/Transaction_Evidence/agent_template_backend/app/examples/ic_examples.py

@@ -0,0 +1,34 @@
+"""Exemplos de IC - Item de Controle.
+
+ICs representam eventos de negócio. Eles alimentam Informacional, Curadoria,
+analytics, BigQuery ou qualquer publisher configurado no framework.
+"""
+
+from typing import Any
+
+
+async def exemplo_fatura_consultada(observer: Any, state: dict[str, Any], invoice_id: str) -> None:
+    await observer.emit_ic(
+        "IC.FATURA_CONSULTADA",
+        {
+            "session_id": state.get("conversation_key") or state.get("session_id"),
+            "tenant_id": state.get("tenant_id"),
+            "agent_id": state.get("agent_id"),
+            "invoice_id": invoice_id,
+        },
+        component="examples.ic",
+    )
+
+
+async def exemplo_acao_concluida(observer: Any, state: dict[str, Any], action_name: str, ok: bool) -> None:
+    await observer.emit_ic(
+        "IC.ACAO_CONCLUIDA",
+        {
+            "session_id": state.get("conversation_key") or state.get("session_id"),
+            "tenant_id": state.get("tenant_id"),
+            "agent_id": state.get("agent_id"),
+            "action_name": action_name,
+            "ok": ok,
+        },
+        component="examples.ic",
+    )

Tuning-Performance/Transaction_Evidence/agent_template_backend/app/examples/mcp_examples.py

@@ -0,0 +1,43 @@
+"""Exemplos de MCP + IC.
+
+O AgentRuntimeMixin já possui _collect_mcp_context(), mas este arquivo mostra o
+padrão para chamadas explícitas ao tool_router quando necessário.
+"""
+
+from typing import Any
+
+
+async def exemplo_chamada_mcp(tool_router: Any, observer: Any, state: dict[str, Any], tool_name: str, payload: dict[str, Any]) -> Any:
+    session_id = state.get("conversation_key") or state.get("session_id")
+
+    await observer.emit_ic(
+        "IC.MCP_TOOL_CALLED",
+        {
+            "session_id": session_id,
+            "tenant_id": state.get("tenant_id"),
+            "agent_id": state.get("agent_id"),
+            "tool_name": tool_name,
+        },
+        component="examples.mcp",
+    )
+
+    result = await tool_router.call(
+        tool_name,
+        payload,
+        business_context=(state.get("context") or {}).get("business_context") or {},
+        original_context=state.get("context") or {},
+    )
+
+    await observer.emit_ic(
+        "IC.TOOL_CALLED",
+        {
+            "session_id": session_id,
+            "tenant_id": state.get("tenant_id"),
+            "agent_id": state.get("agent_id"),
+            "tool_name": tool_name,
+            "ok": getattr(result, "ok", None),
+        },
+        component="examples.mcp",
+    )
+
+    return result

Tuning-Performance/Transaction_Evidence/agent_template_backend/app/examples/noc_examples.py

@@ -0,0 +1,37 @@
+"""Exemplos de NOC.
+
+NOC representa telemetria operacional. O workflow do template já emite NOC.001,
+NOC.005 e NOC.006. Estes exemplos mostram eventos adicionais que a squad pode
+emitir em pontos críticos.
+"""
+
+from typing import Any
+
+
+async def exemplo_api_invalida(observer: Any, state: dict[str, Any], api_url: str, status_code: int, latency_ms: int) -> None:
+    await observer.emit_noc(
+        "002",
+        {
+            "session_id": state.get("conversation_key") or state.get("session_id"),
+            "tenant_id": state.get("tenant_id"),
+            "agent_id": state.get("agent_id"),
+            "apiUrl": api_url,
+            "statusCode": status_code,
+            "latencyMs": latency_ms,
+        },
+        component="examples.noc",
+    )
+
+
+async def exemplo_latencia_banco(observer: Any, state: dict[str, Any], resource_name: str, latency_ms: int) -> None:
+    await observer.emit_noc(
+        "003",
+        {
+            "session_id": state.get("conversation_key") or state.get("session_id"),
+            "tenant_id": state.get("tenant_id"),
+            "agent_id": state.get("agent_id"),
+            "resourceName": resource_name,
+            "latencyMs": latency_ms,
+        },
+        component="examples.noc",
+    )

Tuning-Performance/Transaction_Evidence/agent_template_backend/app/examples/observer_examples.py

@@ -0,0 +1,28 @@
+"""Resumo prático do Observer corporativo.
+
+Use este arquivo como cola rápida para IC, NOC e GRL.
+"""
+
+from typing import Any
+
+
+async def emitir_eventos_basicos(observer: Any, state: dict[str, Any]) -> None:
+    session_id = state.get("conversation_key") or state.get("session_id")
+
+    await observer.emit_ic(
+        "IC.EXEMPLO_NEGOCIO",
+        {"session_id": session_id, "agent_id": state.get("agent_id")},
+        component="examples.observer",
+    )
+
+    await observer.emit_noc(
+        "EXEMPLO_OPERACIONAL",
+        {"session_id": session_id, "agent_id": state.get("agent_id")},
+        component="examples.observer",
+    )
+
+    await observer.emit_grl(
+        "OBSERVE",
+        {"session_id": session_id, "agent_id": state.get("agent_id"), "rail_code": "CUSTOM"},
+        component="examples.observer",
+    )

Tuning-Performance/Transaction_Evidence/agent_template_backend/app/main.py

@@ -0,0 +1,647 @@
+from __future__ import annotations
+
+import logging
+from uuid import uuid4
+import time
+
+from fastapi import FastAPI, HTTPException, Request
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel
+
+from agent_framework.channels.base import ChannelResponse
+from agent_framework.channels.gateway import ChannelGateway
+from agent_framework.channels.interruption import classify_processing_interruption, evaluate_interruption
+from agent_framework.channels.transcription import fix_whole_utterance_transcription
+from agent_framework.config.agent_registry import AgentProfileRegistry
+from agent_framework.config.settings import settings
+from agent_framework.analytics.factory import create_analytics_publisher
+from agent_framework.observer import configure as configure_global_observer
+from agent_framework.llm.providers import create_llm
+from agent_framework.memory.message_history import create_memory
+from agent_framework.memory.summary_memory import create_conversation_summary_memory
+from agent_framework.mcp.tool_router import create_mcp_tool_router
+from agent_framework.models.identity import AgentIdentity
+from agent_framework.identity import IdentityResolver, BusinessContext
+from agent_framework.models.session import ChatMessage, SessionContext
+from agent_framework.observability.telemetry import Telemetry
+from agent_framework.observability.context import set_observability_context, clear_observability_context
+from agent_framework.repositories.session_repository import create_session_repository
+from agent_framework.checkpoints.checkpoint_repository import create_checkpoint_repository
+from agent_framework.cache.cache import create_cache
+from agent_framework.billing.usage_repository import create_usage_repository
+from agent_framework.sse.events import SSEHub
+from app.workflows.agent_graph import AgentWorkflow
+from app.observability.telemetry_observer import TelemetryBackedAgentObserver
+
+logging.basicConfig(level=settings.LOG_LEVEL)
+logger = logging.getLogger("agent_template_backend")
+
+app = FastAPI(title="Agent Template Backend FIRST-ready")
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=[o.strip() for o in settings.CORS_ORIGINS.split(",")],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+telemetry = Telemetry(settings)
+usage_repository = create_usage_repository(settings)
+llm = create_llm(settings, telemetry=telemetry, usage_repository=usage_repository)
+memory = create_memory(settings)
+summary_memory = create_conversation_summary_memory(settings, message_history=memory, llm=llm, telemetry=telemetry)
+sessions = create_session_repository(settings)
+checkpoints = create_checkpoint_repository(settings)
+cache = create_cache(settings, telemetry=telemetry)
+gateway = ChannelGateway(input_mode=settings.FRAMEWORK_CHANNEL_INPUT_MODE)
+analytics = create_analytics_publisher(settings)
+observer = TelemetryBackedAgentObserver(telemetry=telemetry)
+configure_global_observer({
+    "enabled": getattr(settings, "ENABLE_ANALYTICS", False),
+    "providers": getattr(settings, "ANALYTICS_PROVIDERS", "oci_streaming"),
+    "topic_path": getattr(settings, "GCP_PUBSUB_TOPIC_PATH", None) or getattr(settings, "AGENT_PUBSUB_TOPIC", None),
+})
+tool_router = create_mcp_tool_router(settings, telemetry=telemetry)
+identity_resolver = IdentityResolver.from_yaml(settings.IDENTITY_CONFIG_PATH)
+agent_profiles = AgentProfileRegistry(settings)
+sse_hub = SSEHub(settings, telemetry=telemetry)
+workflow = AgentWorkflow(llm, memory, telemetry, analytics, settings, observer=observer, tool_router=tool_router, summary_memory=summary_memory)
+
+logger.info("LLM provider carregado: %s", llm.__class__.__name__)
+logger.info("Langfuse habilitado: %s host=%s", telemetry.is_enabled(), settings.LANGFUSE_HOST)
+logger.info("Analytics habilitado: %s providers=%s", getattr(settings, "ENABLE_ANALYTICS", False), getattr(settings, "ANALYTICS_PROVIDERS", ""))
+logger.info("Agentes disponíveis: %s", [p.agent_id for p in agent_profiles.list_profiles()])
+logger.info("Framework channel input mode: %s", gateway.input_mode)
+
+@app.middleware("http")
+async def observability_context_middleware(request: Request, call_next):
+    clear_observability_context()
+    request_id = request.headers.get("x-request-id") or str(uuid4())
+    set_observability_context(
+        request_id=request_id,
+        channel=request.headers.get("x-channel") or "http",
+        ura_call_id=request.headers.get("x-ura-call-id"),
+    )
+    started = time.time()
+    try:
+        response = await call_next(request)
+        response.headers["x-request-id"] = request_id
+        await telemetry.event("http.request.completed", {
+            "method": request.method,
+            "path": request.url.path,
+            "status_code": response.status_code,
+            "duration_ms": int((time.time() - started) * 1000),
+        }, kind="http")
+        return response
+    except Exception as exc:
+        await telemetry.event("http.request.failed", {
+            "method": request.method,
+            "path": request.url.path,
+            "error": str(exc),
+            "duration_ms": int((time.time() - started) * 1000),
+        }, kind="http")
+        raise
+    finally:
+        clear_observability_context()
+
+
+class GatewayRequest(BaseModel):
+    channel: str = "web"
+    payload: dict
+    agent_id: str | None = None
+    tenant_id: str | None = None
+
+
+def _metadata_value(payload: dict, key: str):
+    metadata = payload.get("metadata")
+    if isinstance(metadata, dict):
+        return metadata.get(key)
+    return None
+
+
+def _extract_workflow_id(payload: dict) -> str | None:
+    return (
+        payload.get("workflow_id")
+        or payload.get("workflowId")
+        or _metadata_value(payload, "workflow_id")
+        or _metadata_value(payload, "workflowId")
+    )
+
+
+def _format_root_span_name(template: str | None, values: dict) -> str:
+    template = template or "agent.gateway_message"
+    try:
+        return template.format(**{k: v or "unknown" for k, v in values.items()})
+    except Exception:
+        logger.warning("LANGFUSE_ROOT_SPAN_NAME inválido: %s", template)
+        return "agent.gateway_message"
+
+
+def _resolve_identity(req: GatewayRequest, msg) -> tuple[AgentIdentity, dict, BusinessContext, list[str]]:
+    payload = req.payload or {}
+    context = dict(msg.context or {})
+    tenant_id = req.tenant_id or payload.get("tenant_id") or context.get("tenant_id") or "default"
+    agent_id = req.agent_id or payload.get("agent_id") or context.get("agent_id") or agent_profiles.default_agent_id
+    profile = agent_profiles.get(agent_id)
+
+    # 1) Identidade técnica do framework: isola tenant/agente/sessão.
+    context.update({"tenant_id": tenant_id, "agent_id": profile.agent_id, "agent_profile": profile.__dict__})
+    identity = AgentIdentity.from_context(context, session_id=msg.session_id)
+
+    # 2) Identidade de negócio: chaves canônicas vindas do front/canal.
+    #    Estas chaves são estáveis na sessão e seguem até agentes e MCP Router.
+    previous_business_context = context.get("business_context") or context.get("identity") or {}
+    business_context = identity_resolver.resolve(
+        {**payload, **context},
+        session_id=identity.conversation_key(),
+        previous=previous_business_context,
+    )
+    missing_identity_keys = identity_resolver.validate(business_context)
+    context.update({
+        "business_context": business_context.model_dump(),
+        "business_keys": business_context.to_context_dict(),
+        "identity_missing": missing_identity_keys,
+        "conversation_key": identity.conversation_key(),
+        "original_session_id": msg.session_id,
+    })
+    return identity, context, business_context, missing_identity_keys
+
+
+async def _process_gateway_message(req: GatewayRequest, emit_sse: bool = False) -> dict:
+    try:
+        msg = await gateway.normalize(req.channel, req.payload)
+    except ValueError as exc:
+        raise HTTPException(status_code=422, detail=str(exc)) from exc
+    payload = req.payload or {}
+    identity, normalized_context, business_context, missing_identity_keys = _resolve_identity(req, msg)
+    agent_session_id = identity.conversation_key()
+    message_id = payload.get("message_id") or str(uuid4())
+    workflow_id = _extract_workflow_id(payload)
+    set_observability_context(
+        session_id=agent_session_id,
+        user_id=msg.user_id,
+        tenant_id=identity.tenant_id,
+        agent_id=identity.agent_id,
+        channel=msg.channel,
+        message_id=message_id,
+        workflow_id=workflow_id,
+        ura_call_id=payload.get("ura_call_id") or normalized_context.get("ura_call_id") or business_context.interaction_key,
+    )
+
+    stream = sse_hub.stream_for(agent_session_id)
+    async with stream.lock:
+        await sse_hub.emit(agent_session_id, "flow.start", {"session_id": agent_session_id, "message_id": message_id, "agent_id": identity.agent_id}) if emit_sse else None
+
+        session = await sessions.get(agent_session_id)
+        if not session:
+            context_fields = {
+                k: v
+                for k, v in normalized_context.items()
+                if k in SessionContext.model_fields
+                and k not in {"tenant_id", "agent_id", "session_id", "user_id", "channel", "channel_id"}
+            }
+            session = SessionContext(
+                tenant_id=identity.tenant_id,
+                agent_id=identity.agent_id,
+                session_id=agent_session_id,
+                user_id=msg.user_id,
+                channel=msg.channel,
+                channel_id=msg.channel_id,
+                **context_fields,
+            )
+
+        session.tenant_id = identity.tenant_id
+        session.agent_id = identity.agent_id
+        session.channel = msg.channel
+        session.channel_id = msg.channel_id or session.channel_id
+        await sessions.upsert(session)
+        session.metadata = {
+            **(session.metadata or {}),
+            "business_context": business_context.model_dump(),
+            "identity_missing": missing_identity_keys,
+            "original_context": normalized_context,
+        }
+        await sessions.upsert(session)
+
+        fixed_message_text = fix_whole_utterance_transcription(msg.text)
+        if fixed_message_text != msg.text:
+            await telemetry.event(
+                "channel.transcription.fixed",
+                {
+                    "session_id": agent_session_id,
+                    "original_text": msg.text,
+                    "fixed_text": fixed_message_text,
+                },
+            )
+
+        interruption = evaluate_interruption(
+            payload=payload,
+            message_text=fixed_message_text,
+            session_metadata=session.metadata,
+            terminal_fallback_text=getattr(settings, "POST_FINALIZE_REPLAY_MESSAGE", ""),
+        )
+        if interruption.action == "classify":
+            prior_history = await memory.list(agent_session_id)
+            prior_user_text = ""
+            for prior in reversed(prior_history):
+                role = getattr(prior, "role", None)
+                content = getattr(prior, "content", "")
+                if str(role or "") == "user" and str(content or "").strip():
+                    prior_user_text = str(content).strip()
+                    break
+            regenerate = await classify_processing_interruption(
+                llm,
+                original_agent=interruption.replay_text,
+                original_client=prior_user_text,
+                supplement_client=interruption.text,
+            )
+            await telemetry.event(
+                "channel.processing_interruption.classified",
+                {
+                    "session_id": agent_session_id,
+                    "regenerate": regenerate,
+                    "profile_name": "processing_interruption_classifier",
+                },
+            )
+            if regenerate:
+                interruption.action = "process"
+                interruption.reason = "classifier_result_1"
+            else:
+                interruption.action = "replay"
+                interruption.reason = "classifier_result_0"
+
+        if interruption.action == "replay":
+            response = ChannelResponse(
+                channel=msg.channel,
+                session_id=agent_session_id,
+                text=interruption.replay_text,
+                metadata={
+                    "channel_id": msg.channel_id,
+                    "tenant_id": identity.tenant_id,
+                    "agent_id": identity.agent_id,
+                    "original_session_id": msg.session_id,
+                    "conversation_key": agent_session_id,
+                    "workflow_id": workflow_id,
+                    "message_id": message_id,
+                    "replay": True,
+                    "replay_reason": interruption.reason,
+                    "is_interruptible": interruption.is_interruptible,
+                    "framework_short_circuit": True,
+                    "terminal_status": interruption.terminal_status,
+                    "llm_called": False,
+                    "tools_called": False,
+                    "guardrails_called": False,
+                },
+            )
+            rendered = await gateway.render(response)
+            await telemetry.event("gateway.message.replayed", {"session_id": agent_session_id, "reason": interruption.reason})
+            await sse_hub.emit(agent_session_id, "message.responded", rendered) if emit_sse else None
+            return rendered
+
+        effective_text = interruption.text
+        await sse_hub.emit(agent_session_id, "session.upserted", {"session_id": agent_session_id, "business_context": business_context.model_dump()}) if emit_sse else None
+
+        await memory.append(
+            agent_session_id,
+            ChatMessage(
+                role="user",
+                content=effective_text,
+                metadata={
+                    **normalized_context,
+                    "agent_id": identity.agent_id,
+                    "tenant_id": identity.tenant_id,
+                    "message_id": message_id,
+                    "business_context": business_context.model_dump(),
+                    "identity_missing": missing_identity_keys,
+                },
+            ),
+        )
+        await sse_hub.emit(agent_session_id, "message.received", {"session_id": agent_session_id, "role": "user"}) if emit_sse else None
+        history = [m.model_dump(mode="json") for m in await memory.list(agent_session_id)]
+
+        cms_input = {
+            "channel": req.channel,
+            "tenant_id": req.tenant_id,
+            "agent_id": req.agent_id,
+            "payload": payload,
+        }
+        trace_context = {
+            "text": effective_text,
+            "channel": msg.channel,
+            "channel_id": msg.channel_id,
+            "tenant_id": identity.tenant_id,
+            "agent_id": identity.agent_id,
+            "conversation_key": agent_session_id,
+            "workflow_id": workflow_id,
+            "message_id": message_id,
+            "business_context": business_context.model_dump(),
+            "identity_missing": missing_identity_keys,
+        }
+        root_span_name = _format_root_span_name(
+            getattr(settings, "LANGFUSE_ROOT_SPAN_NAME", "agent.gateway_message"),
+            {
+                "workflow_id": workflow_id,
+                "channel": msg.channel,
+                "agent_id": identity.agent_id,
+                "tenant_id": identity.tenant_id,
+            },
+        )
+        root_tags = ["agent-template", msg.channel, f"agent:{identity.agent_id}", f"tenant:{identity.tenant_id}"]
+        if workflow_id:
+            root_tags.append(f"workflow:{workflow_id}")
+
+        async with telemetry.span(
+            root_span_name,
+            session_id=agent_session_id,
+            user_id=session.user_id,
+            channel=msg.channel,
+            workflow_id=workflow_id,
+            input=cms_input,
+            tags=root_tags,
+            _root_span=True,
+        ) as root_span:
+            await telemetry.event("gateway.message.received", trace_context)
+            await sse_hub.emit(agent_session_id, "workflow.started", trace_context) if emit_sse else None
+            result = await workflow.ainvoke(
+                {
+                    "tenant_id": identity.tenant_id,
+                    "agent_id": identity.agent_id,
+                    "session_id": agent_session_id,
+                    "conversation_key": agent_session_id,
+                    "workflow_id": workflow_id,
+                    "agent_profile": normalized_context["agent_profile"],
+                    # Chave estável de LTM. Nunca use session_id como identidade de longo prazo.
+                    "long_term_memory_subject_key": business_context.customer_key or session.user_id,
+                    "customer_key": business_context.customer_key,
+                    "user_id": session.user_id,
+                    "business_context": business_context.model_dump(),
+                    "user_text": effective_text,
+                    "history": history,
+                    "context": {
+                        **normalized_context,
+                        "session": session.model_dump(mode="json"),
+                        "original_session_id": msg.session_id,
+                        "session_id": agent_session_id,
+                        "conversation_key": agent_session_id,
+                        "workflow_id": workflow_id,
+                        "user_id": session.user_id,
+                        "channel": msg.channel,
+                        "message_id": message_id,
+                        "business_context": business_context.model_dump(),
+                        "business_keys": business_context.to_context_dict(),
+                        "identity_missing": missing_identity_keys,
+                    },
+                }
+            )
+
+            await checkpoints.put(agent_session_id, {"state": result, "message_id": message_id})
+            await sse_hub.emit(agent_session_id, "workflow.completed", {"session_id": agent_session_id, "route": result.get("route"), "intent": result.get("intent")}) if emit_sse else None
+
+            answer = result.get("final_answer") or result.get("answer") or ""
+            await memory.append(
+                agent_session_id,
+                ChatMessage(
+                    role="assistant",
+                    content=answer,
+                    metadata={
+                        "tenant_id": identity.tenant_id,
+                        "agent_id": identity.agent_id,
+                        "message_id": f"assistant-{message_id}",
+                        "route": result.get("route"),
+                        "intent": result.get("intent"),
+                        "route_decision": result.get("route_decision"),
+                        "judges": result.get("judge_results"),
+                    },
+                ),
+            )
+
+            terminal_status = str(result.get("terminal_status") or "").strip()
+            session_ended = bool(result.get("session_ended")) or bool(terminal_status)
+            session.metadata = {
+                **(session.metadata or {}),
+                "last_assistant_text": answer,
+                "last_assistant_is_interruptible": bool(result.get("is_interruptible", True)),
+                "last_route": result.get("route"),
+                "last_intent": result.get("intent"),
+                "conversation_closed": session_ended,
+                "terminal_status": terminal_status or ("resolvido" if session_ended else ""),
+                "terminal_replay_text": answer if session_ended else "",
+            }
+            await sessions.upsert(session)
+
+            await telemetry.event(
+                "gateway.message.responded",
+                {
+                    "session_id": agent_session_id,
+                    "tenant_id": identity.tenant_id,
+                    "agent_id": identity.agent_id,
+                    "route": result.get("route"),
+                    "intent": result.get("intent"),
+                    "answer_chars": len(answer),
+                },
+            )
+
+            response = ChannelResponse(
+                channel=msg.channel,
+                session_id=agent_session_id,
+                text=answer,
+                metadata={
+                    "channel_id": msg.channel_id,
+                    "tenant_id": identity.tenant_id,
+                    "agent_id": identity.agent_id,
+                    "original_session_id": msg.session_id,
+                    "conversation_key": agent_session_id,
+                    "workflow_id": workflow_id,
+                    "message_id": message_id,
+                    "route": result.get("route"),
+                    "intent": result.get("intent"),
+                    "route_decision": result.get("route_decision"),
+                    "domain": result.get("domain"),
+                    "mcp_tools": result.get("mcp_tools"),
+                    "mcp_results": result.get("mcp_results"),
+                    "transaction_evidence": result.get("relevant_transaction_evidence", []),
+                    "business_context": business_context.model_dump(),
+                    "identity_missing": missing_identity_keys,
+                    "judges": result.get("judge_results"),
+                    "guardrails": result.get("guardrail_decisions"),
+                    "long_term_memory": {
+                        "subject_key": business_context.customer_key or session.user_id,
+                        "loaded": result.get("long_term_memories", []),
+                        "context": result.get("long_term_memory_context", ""),
+                        "load_error": result.get("long_term_memory_load_error"),
+                        "write_result": result.get("long_term_memory_write_result", {}),
+                    },
+                },
+            )
+            rendered = await gateway.render(response)
+            root_span.set_output(rendered)
+            await sse_hub.emit(agent_session_id, "message.responded", rendered) if emit_sse else None
+            await sse_hub.emit(agent_session_id, "flow.end", {"session_id": agent_session_id, "message_id": message_id}) if emit_sse else None
+            return rendered
+
+
+@app.get("/health")
+async def health():
+    return {
+        "status": "ok",
+        "llm_provider": settings.LLM_PROVIDER,
+        "llm_class": llm.__class__.__name__,
+        "langfuse_enabled": telemetry.is_enabled(),
+        "agents": [p.agent_id for p in agent_profiles.list_profiles()],
+        "default_agent_id": agent_profiles.default_agent_id,
+        "routing_mode": settings.ROUTING_MODE,
+        "sse_enabled": settings.ENABLE_SSE,
+        "session_repository": settings.SESSION_REPOSITORY_PROVIDER,
+        "memory_repository": settings.MEMORY_REPOSITORY_PROVIDER,
+        "long_term_memory": {
+            "enabled": getattr(settings, "ENABLE_LONG_TERM_MEMORY", False),
+            "provider": getattr(settings, "LONG_TERM_MEMORY_PROVIDER", None),
+            "sqlite_path": getattr(settings, "LONG_TERM_MEMORY_SQLITE_PATH", None),
+            "table": getattr(settings, "LONG_TERM_MEMORY_TABLE", None),
+            "auto_extract": getattr(settings, "LONG_TERM_MEMORY_AUTO_EXTRACT", None),
+            "inject_context": getattr(settings, "LONG_TERM_MEMORY_INJECT_CONTEXT", None),
+        },
+        "checkpoint_repository": settings.CHECKPOINT_REPOSITORY_PROVIDER,
+        "usage_repository": settings.USAGE_REPOSITORY_PROVIDER,
+        "identity_config_path": settings.IDENTITY_CONFIG_PATH,
+        "mcp_parameter_mapping_path": settings.MCP_PARAMETER_MAPPING_PATH,
+        "framework_channel_input_mode": settings.FRAMEWORK_CHANNEL_INPUT_MODE,
+        "legacy_channel_gateway_mode": settings.CHANNEL_GATEWAY_MODE,
+    }
+
+
+@app.get("/agents")
+async def list_agents():
+    return {"default_agent_id": agent_profiles.default_agent_id, "agents": [p.__dict__ for p in agent_profiles.list_profiles()]}
+
+
+@app.get("/debug/env")
+async def debug_env():
+    return {
+        "APP_ENV": settings.APP_ENV,
+        "LLM_PROVIDER": settings.LLM_PROVIDER,
+        "ENABLE_LANGFUSE": settings.ENABLE_LANGFUSE,
+        "LANGFUSE_HOST": settings.LANGFUSE_HOST,
+        "TELEMETRY_ENABLED": telemetry.is_enabled(),
+        "SQLITE_DB_PATH": settings.SQLITE_DB_PATH,
+        "SESSION_REPOSITORY_PROVIDER": settings.SESSION_REPOSITORY_PROVIDER,
+        "MEMORY_REPOSITORY_PROVIDER": settings.MEMORY_REPOSITORY_PROVIDER,
+        "CHECKPOINT_REPOSITORY_PROVIDER": settings.CHECKPOINT_REPOSITORY_PROVIDER,
+        "AGENTS_CONFIG_PATH": settings.AGENTS_CONFIG_PATH,
+        "ROUTING_CONFIG_PATH": settings.ROUTING_CONFIG_PATH,
+        "ROUTING_MODE": settings.ROUTING_MODE,
+        "FRAMEWORK_CHANNEL_INPUT_MODE": settings.FRAMEWORK_CHANNEL_INPUT_MODE,
+        "CHANNEL_GATEWAY_MODE": settings.CHANNEL_GATEWAY_MODE,
+    }
+
+
+@app.get("/test-llm")
+async def test_llm():
+    async with telemetry.span("debug.test_llm", input={"message": "Diga apenas OK"}):
+        answer = await llm.ainvoke([
+            {"role": "system", "content": "Responda de forma curta."},
+            {"role": "user", "content": "Diga apenas OK"},
+        ])
+    telemetry.flush()
+    return {"provider": llm.__class__.__name__, "answer": answer}
+
+
+@app.post("/debug/route")
+async def debug_route(req: GatewayRequest):
+    msg = await gateway.normalize(req.channel, req.payload)
+    identity, context, business_context, missing_identity_keys = _resolve_identity(req, msg)
+    state = {
+        "tenant_id": identity.tenant_id,
+        "agent_id": identity.agent_id,
+        "session_id": msg.session_id or "debug-session",
+        "conversation_key": identity.conversation_key(),
+        "agent_profile": context["agent_profile"],
+        "user_text": effective_text,
+        "sanitized_input": msg.text,
+        "history": [],
+        "context": {**context, "session": context.get("session", {}), "channel": msg.channel, "business_context": business_context.model_dump()},
+    }
+    if settings.ROUTING_MODE == "supervisor":
+        plan = await workflow.supervisor.route_plan(state)
+        return {"mode": "supervisor", "route": "supervisor_agent", "agents": plan.agents, "intent": plan.intent, "confidence": plan.confidence, "reason": plan.reason, "metadata": plan.metadata}
+    decision = await workflow.router.route(state)
+    data = decision.model_dump(mode="json")
+    data["mode"] = "router"
+    return data
+
+
+
+
+@app.post("/debug/identity")
+async def debug_identity(req: GatewayRequest):
+    msg = await gateway.normalize(req.channel, req.payload)
+    identity, context, business_context, missing_identity_keys = _resolve_identity(req, msg)
+    return {
+        "technical_identity": {
+            "tenant_id": identity.tenant_id,
+            "agent_id": identity.agent_id,
+            "conversation_key": identity.conversation_key(),
+            "original_session_id": msg.session_id,
+        },
+        "business_context": business_context.model_dump(),
+        "identity_missing": missing_identity_keys,
+        "context_keys": sorted(context.keys()),
+    }
+
+@app.get("/debug/usage")
+async def debug_usage(tenant_id: str | None = None, session_id: str | None = None):
+    return await usage_repository.summarize(tenant_id=tenant_id, session_id=session_id)
+
+
+@app.get("/debug/mcp/tools")
+async def debug_mcp_tools():
+    return {"enabled": tool_router.enabled, "tools": tool_router.describe_tools()}
+
+
+@app.post("/debug/mcp/call/{tool_name}")
+async def debug_mcp_call(tool_name: str, arguments: dict | None = None):
+    arguments = arguments or {}
+    ctx = arguments.get("business_context") or arguments.get("identity") or {}
+    result = await tool_router.call(
+        tool_name,
+        arguments,
+        business_context=ctx,
+        original_context=arguments,
+    )
+    return result.model_dump(mode="json")
+
+
+@app.post("/gateway/message")
+async def gateway_message(req: GatewayRequest):
+    return await _process_gateway_message(req, emit_sse=False)
+
+
+@app.post("/gateway/message/sse")
+async def gateway_message_sse(req: GatewayRequest):
+    return await _process_gateway_message(req, emit_sse=True)
+
+
+@app.get("/gateway/events/{session_id}")
+async def gateway_events(session_id: str, request: Request):
+    last = request.headers.get("last-event-id") or request.query_params.get("last_event_id") or "0"
+    return StreamingResponse(
+        sse_hub.subscribe(session_id, int(last)),
+        media_type="text/event-stream",
+        headers={"Cache-Control": "no-cache", "Connection": "keep-alive", "X-Accel-Buffering": "no"},
+    )
+
+
+@app.get("/sessions/{session_id}/messages")
+async def get_session_messages(session_id: str, limit: int = 50):
+    return {"session_id": session_id, "messages": [m.model_dump(mode="json") for m in await memory.list(session_id, limit)]}
+
+
+@app.get("/sessions/{session_id}/checkpoint")
+async def get_session_checkpoint(session_id: str):
+    return {"session_id": session_id, "checkpoint": await checkpoints.get_latest(session_id)}
+
+
+@app.on_event("shutdown")
+async def shutdown():
+    telemetry.shutdown()

Tuning-Performance/Transaction_Evidence/agent_template_backend/app/mcp_gateway_client_factory.py

@@ -0,0 +1,16 @@
+from __future__ import annotations
+
+import os
+
+from agent_framework.gateways import MCPGatewayClient
+
+
+def build_mcp_gateway_client() -> MCPGatewayClient | None:
+    if os.getenv("MCP_GATEWAY_ENABLED", "true").lower() != "true":
+        return None
+
+    return MCPGatewayClient(
+        base_url=os.getenv("MCP_GATEWAY_URL", "http://localhost:8300"),
+        token=os.getenv("MCP_GATEWAY_TOKEN") or None,
+        timeout_seconds=int(os.getenv("MCP_GATEWAY_TIMEOUT_SECONDS", "60")),
+    )

Tuning-Performance/Transaction_Evidence/agent_template_backend/app/observability/telemetry_observer.py

@@ -0,0 +1,84 @@
+from __future__ import annotations
+
+"""Observer adapter that emits IC/NOC/GRL through framework Telemetry only.
+
+This avoids a second Langfuse root trace created by AgentObserver ->
+AnalyticsPublisher while preserving the events inside the active request span.
+"""
+
+from datetime import datetime, timezone
+from typing import Any
+
+
+def _normalize_ic_code(code: str) -> str:
+    code = str(code or "UNKNOWN").strip()
+    return code if code.startswith(("IC.", "AGA.", "NOC.", "GRL.")) else f"IC.{code}"
+
+
+def _normalize_noc_code(code: str) -> str:
+    code = str(code or "UNKNOWN").strip()
+    return code if code.startswith("NOC.") else f"NOC.{code}"
+
+
+def _normalize_grl_code(code: str) -> str:
+    code = str(code or "UNKNOWN").strip()
+    return code if code.startswith("GRL.") else f"GRL.{code}"
+
+
+def _kind_for(event_type: str) -> str:
+    if event_type.startswith(("IC.", "AGA.")):
+        return "ic"
+    if event_type.startswith("NOC."):
+        return "noc"
+    if event_type.startswith("GRL."):
+        return "grl"
+    return "event"
+
+
+class TelemetryBackedAgentObserver:
+    """Drop-in subset of AgentObserver backed by Telemetry.event.
+
+    Do not publish through AnalyticsPublisher here. Analytics publishing may be
+    configured with a Langfuse provider, and that path creates an extra root
+    trace for business events such as IC.AGENT_COMPLETED/NOC.006. Telemetry.event
+    uses the active span/trace context, so these events appear inside the single
+    request trace.
+    """
+
+    def __init__(self, telemetry: Any, *, source: str = "agent_framework") -> None:
+        self.telemetry = telemetry
+        self.source = source
+
+    async def emit(
+        self,
+        event_type: str,
+        payload: dict[str, Any] | None = None,
+        *,
+        metadata: dict[str, Any] | None = None,
+        source: str | None = None,
+    ) -> dict[str, Any]:
+        body = dict(payload or {})
+        meta = dict(metadata or {})
+        body.setdefault("tag", event_type)
+        event = {
+            "eventType": event_type,
+            "source": source or self.source,
+            "eventDate": datetime.now(timezone.utc).isoformat(),
+            "body": body,
+            "metadata": meta,
+        }
+        try:
+            await self.telemetry.event(event_type, event, kind=_kind_for(event_type))
+        except TypeError:
+            # Compatibility with older Telemetry.event signatures.
+            await self.telemetry.event(event_type, event)
+        return event
+
+    async def emit_ic(self, code: str, payload: dict[str, Any] | None = None, **metadata: Any) -> dict[str, Any]:
+        return await self.emit(_normalize_ic_code(code), payload, metadata={**metadata, "ic": True})
+
+    async def emit_noc(self, code: str, payload: dict[str, Any] | None = None, **metadata: Any) -> dict[str, Any]:
+        return await self.emit(_normalize_noc_code(code), payload, metadata={**metadata, "noc": True})
+
+    async def emit_grl(self, code: str, payload: dict[str, Any] | None = None, **metadata: Any) -> dict[str, Any]:
+        return await self.emit(_normalize_grl_code(code), payload, metadata={**metadata, "grl": True})

Tuning-Performance/Transaction_Evidence/agent_template_backend/app/presentation/__init__.py

@@ -0,0 +1,3 @@
+from .tool_renderers import register_tool_renderers
+
+__all__ = ["register_tool_renderers"]

Tuning-Performance/Transaction_Evidence/agent_template_backend/app/presentation/tool_renderers.py

@@ -0,0 +1,74 @@
+from __future__ import annotations
+
+from typing import Any
+
+from agent_framework.presentation import register_tool_response_renderer
+
+
+def _money_brl(value: Any) -> str:
+    try:
+        return f"{float(value):.2f}".replace(".", ",")
+    except (TypeError, ValueError):
+        return str(value)
+
+
+def render_telecom_invoice(*, tool_name: str, result: dict[str, Any], state: dict[str, Any], agent_label: str) -> str | None:
+    return f"[{agent_label}] Fatura consultada: {result}."
+
+
+def render_telecom_plan(*, tool_name: str, result: dict[str, Any], state: dict[str, Any], agent_label: str) -> str | None:
+    plano = result.get("plano")
+    if plano is None:
+        return None
+    parts = [f"[{agent_label}] Seu plano é {plano}"]
+    internet_gb = result.get("internet_gb")
+    status = result.get("status")
+    if internet_gb is not None:
+        parts.append(f"com {internet_gb} GB")
+    if status is not None:
+        parts.append(f"status {status}")
+    return ", ".join(parts) + "."
+
+
+def render_retail_order(*, tool_name: str, result: dict[str, Any], state: dict[str, Any], agent_label: str) -> str | None:
+    order_id = result.get("order_id")
+    status = result.get("status")
+    if order_id is None or status is None:
+        return None
+    lines = [f"[{agent_label}] Pedido {order_id}: status {status}."]
+    total = result.get("valor_total")
+    if total is not None:
+        lines.append(f"Valor total: R$ {_money_brl(total)}.")
+    items = result.get("itens") or []
+    rendered_items: list[str] = []
+    if isinstance(items, list):
+        for item in items:
+            if isinstance(item, dict):
+                value = item.get("descricao") or item.get("nome") or item.get("sku")
+            else:
+                value = item
+            if value not in (None, ""):
+                rendered_items.append(str(value))
+    if rendered_items:
+        lines.append("Itens: " + "; ".join(rendered_items) + ".")
+    return " ".join(lines)
+
+
+def render_retail_delivery(*, tool_name: str, result: dict[str, Any], state: dict[str, Any], agent_label: str) -> str | None:
+    order_id = result.get("order_id")
+    transportadora = result.get("transportadora")
+    codigo = result.get("codigo_rastreio")
+    previsao = result.get("previsao_entrega")
+    if any(v is None for v in (order_id, transportadora, codigo, previsao)):
+        return None
+    return (
+        f"[{agent_label}] Entrega do pedido {order_id}: transportadora {transportadora}, "
+        f"rastreio {codigo}, previsão {previsao}."
+    )
+
+
+def register_tool_renderers() -> None:
+    register_tool_response_renderer("telecom.invoice", render_telecom_invoice)
+    register_tool_response_renderer("telecom.plan", render_telecom_plan)
+    register_tool_response_renderer("retail.order", render_retail_order)
+    register_tool_response_renderer("retail.delivery", render_retail_delivery)

Tuning-Performance/Transaction_Evidence/agent_template_backend/app/state.py

@@ -0,0 +1,56 @@
+from typing import Any, TypedDict
+
+
+class AgentState(TypedDict, total=False):
+    tenant_id: str
+    agent_id: str
+    session_id: str
+    conversation_key: str
+    workflow_id: str
+    agent_profile: dict[str, Any]
+    user_text: str
+    sanitized_input: str
+    route: str
+    intent: str
+    route_decision: dict[str, Any]
+    answer: str
+    final_answer: str
+    history: list[dict[str, Any]]
+    context: dict[str, Any]
+    guardrail_decisions: list[dict[str, Any]]
+    judge_results: list[dict[str, Any]]
+    next_state: str
+    domain: str
+    mcp_tools: list[str]
+    mcp_results: list[dict[str, Any]]
+    available_mcp_tools: list[str]
+    selected_tool_call: dict[str, Any]
+    pending_tool_call: dict[str, Any]
+    transaction_status: str
+    transaction_evidence: list[dict[str, Any]]
+    last_transaction_evidence: dict[str, Any]
+    relevant_transaction_evidence: list[dict[str, Any]]
+    confirmation_required: bool
+    confirmation_received: bool
+    tool_policy_result: dict[str, Any]
+    missing_parameters: list[str]
+    supervisor_plan: dict[str, Any]
+    supervisor_results: list[dict[str, Any]]
+    active_agent: str
+    route_bypassed: bool
+    continuity_signal: dict[str, Any]
+    session_control: str
+    session_ended: bool
+    human_handoff_requested: bool
+    blocked: bool
+    supervisor_action: str
+    supervisor_guidance: str
+    supervisor_attempt: int
+    supervisor_handover_reason: str
+    output_supervisor_results: list[dict[str, Any]]
+    output_guardrails_already_applied: bool
+    long_term_memories: list[dict[str, Any]]
+    long_term_memory_context: str
+    long_term_memory_write_result: dict[str, Any]
+    long_term_memory_subject_key: str
+    long_term_memory_load_error: str

Tuning-Performance/Transaction_Evidence/agent_template_backend/app/workflow_actions/__init__.py

@@ -0,0 +1 @@
+from . import devolucao  # noqa: F401

Tuning-Performance/Transaction_Evidence/agent_template_backend/app/workflow_actions/devolucao.py

@@ -0,0 +1,13 @@
+"""Actions de domínio permanecem no agente; o runtime está no framework."""
+from agent_framework.workflows import workflow_action
+
+
+@workflow_action("validar_pedido")
+async def validar_pedido(params: dict, state: dict) -> dict:
+    return {"valid": bool(params.get("order_id"))}
+
+
+@workflow_action("registrar_devolucao")
+async def registrar_devolucao(params: dict, state: dict) -> dict:
+    # Substitua pela chamada real ao serviço/MCP e use chave idempotente.
+    return {"protocol": f"DEV-{params['order_id']}", "status": "REQUESTED"}

Tuning-Performance/Transaction_Evidence/agent_template_backend/app/workflows/agent_graph.py

@@ -0,0 +1,892 @@
+from agent_framework.checkpoints.langgraph_saver import create_langgraph_checkpointer
+from agent_framework.workflows import END, START, FrameworkStateGraph
+
+from agent_framework.guardrails.pipeline import GuardrailPipeline
+from agent_framework.guardrails.output_supervisor import OutputSupervisor
+from agent_framework.guardrails.rail_action import RailAction
+from agent_framework.guardrails.rail_result import RailResult
+from agent_framework.judges.judge import JudgePipeline
+from agent_framework.routing.enterprise_router import EnterpriseRouter
+from agent_framework.supervisor.supervisor import Supervisor
+from agent_framework.observability.workflow_events import WorkflowTelemetry
+from agent_framework.observability.guardrail_events import GuardrailTelemetry
+from agent_framework.observability.judge_events import JudgeTelemetry
+from agent_framework.observability.langgraph_telemetry import LangGraphDeepTelemetry
+from agent_framework.observability.observer import AgentObserver
+from app.agents.billing_agent import BillingAgent
+from app.agents.product_agent import ProductAgent
+from app.agents.orders_agent import OrdersAgent
+from app.agents.support_agent import SupportAgent
+from app.state import AgentState
+from agent_framework.rag.rag_service import RagService
+from agent_framework.rag.embedding_provider import create_embedding_provider
+from agent_framework.cache.cache import create_cache
+from agent_framework.memory.long_term_memory import create_long_term_memory_manager
+
+
+class LegacyOutputGuardrailRail:
+    """Adapter: reutiliza GuardrailPipeline.run_output dentro do OutputSupervisor novo.
+
+    O framework antigo retornava decisões allowed=True/False. O OutputSupervisor
+    corporativo trabalha com RailAction (allow/sanitize/retry/block/handover).
+    Este adapter evita reescrever todos os rails agora e mantém compatibilidade.
+    """
+
+    code = "LEGACY_OUTPUT_GUARDRAILS"
+
+    def __init__(self, pipeline: GuardrailPipeline):
+        self.pipeline = pipeline
+
+    async def evaluate(self, candidate: str, context: dict):
+        final, decisions = await self.pipeline.run_output(candidate, context)
+        serialized = [d.model_dump() for d in decisions]
+
+        blocked = [d for d in decisions if not getattr(d, "allowed", True)]
+        if blocked:
+            first = blocked[0]
+            code = (getattr(first, "code", "") or "").upper()
+            action = RailAction.RETRY if code in {"REVPREC", "CMP", "SCO", "GND"} else RailAction.BLOCK
+            return RailResult(
+                code=code or self.code,
+                action=action,
+                reason=getattr(first, "reason", "Resposta bloqueada por guardrail de saída"),
+                guidance=getattr(first, "reason", "Regerar resposta seguindo as políticas de saída."),
+                sanitized_text=final,
+                metadata={"legacy_decisions": serialized},
+            )
+
+        if final != candidate:
+            return RailResult(
+                code=self.code,
+                action=RailAction.SANITIZE,
+                reason="Resposta sanitizada por guardrail de saída legado.",
+                sanitized_text=final,
+                metadata={"legacy_decisions": serialized},
+            )
+
+        return RailResult(
+            code=self.code,
+            action=RailAction.ALLOW,
+            reason="Resposta aprovada pelos guardrails de saída legados.",
+            sanitized_text=final,
+            metadata={"legacy_decisions": serialized},
+        )
+
+
+class AgentWorkflow:
+    """Workflow principal com dois modos de roteamento.
+
+    Modos suportados por configuração:
+      ROUTING_MODE=router
+        input_guardrails -> routing_decision/EnterpriseRouter -> 1 agente -> output_guardrails
+
+      ROUTING_MODE=supervisor
+        input_guardrails -> routing_decision/Supervisor -> supervisor_agent -> N agentes -> consolidação
+
+    Em ambos os modos, memória/checkpoint/session usam tenant_id:agent_id:session_id.
+    """
+
+    def __init__(self, llm, memory, telemetry, analytics, settings, observer: AgentObserver | None = None, tool_router=None, summary_memory=None):
+        self.llm = llm
+        self.memory = memory
+        self.telemetry = telemetry
+        self.analytics = analytics
+        self.observer = observer or AgentObserver(analytics=analytics)
+        self.settings = settings
+        self.tool_router = tool_router
+        self.summary_memory = summary_memory
+        self.long_term_memory_manager = create_long_term_memory_manager(settings, telemetry=telemetry)
+        self.guardrails = GuardrailPipeline(
+            observer=self.observer,
+            enable_parallel=bool(getattr(settings, "ENABLE_PARALLEL_GUARDRAILS", True)),
+            fail_fast=bool(getattr(settings, "GUARDRAILS_FAIL_FAST", True)),
+        )
+        self.output_supervisor_engine = OutputSupervisor(
+            rails=[LegacyOutputGuardrailRail(self.guardrails)],
+            observer=self.observer,
+            max_retries=int(getattr(settings, "OUTPUT_SUPERVISOR_MAX_RETRIES", 3)),
+            enable_parallel=bool(getattr(settings, "ENABLE_PARALLEL_GUARDRAILS", True)),
+            fail_fast=bool(getattr(settings, "GUARDRAILS_FAIL_FAST", True)),
+        )
+        self.judges = JudgePipeline()
+        self.supervisor = Supervisor()
+        self.workflow_telemetry = WorkflowTelemetry(telemetry)
+        self.guardrail_telemetry = GuardrailTelemetry(telemetry)
+        self.judge_telemetry = JudgeTelemetry(telemetry)
+        self.langgraph_telemetry = LangGraphDeepTelemetry(telemetry)
+        self.cache = create_cache(settings)
+        self.embedding_provider = create_embedding_provider(settings)
+        self.rag_service = RagService(settings, embedding_provider=self.embedding_provider, telemetry=telemetry)
+        self.router = EnterpriseRouter(settings, llm=llm, telemetry=telemetry)
+        agent_kwargs = {"telemetry": telemetry, "tool_router": getattr(self, "tool_router", None), "rag_service": self.rag_service, "cache": self.cache, "settings": settings, "observer": self.observer, "memory": memory, "summary_memory": summary_memory}
+        self.billing = BillingAgent(llm, **agent_kwargs)
+        self.product = ProductAgent(llm, **agent_kwargs)
+        self.orders = OrdersAgent(llm, **agent_kwargs)
+        self.support = SupportAgent(llm, **agent_kwargs)
+
+        # The existing agent constructors intentionally keep their stable API.
+        # Long-term memory is injected as a runtime capability after creation.
+        for agent in (self.billing, self.product, self.orders, self.support):
+            agent.long_term_memory_manager = self.long_term_memory_manager
+        self.graph = self._build_graph()
+
+    def _node(self, name, fn):
+        async def _wrapped(state):
+            async with self.langgraph_telemetry.node(name, state):
+                return await fn(state)
+        return _wrapped
+
+    def _build_graph(self):
+        builder = FrameworkStateGraph(AgentState)
+        builder.add_node("input_guardrails", self._node("input_guardrails", self.input_guardrails))
+        builder.add_node("load_long_term_memory", self._node("load_long_term_memory", self.load_long_term_memory))
+        builder.add_node("routing_decision", self._node("routing_decision", self.routing_decision))
+        builder.add_node("billing_agent", self._node("billing_agent", self.billing_agent))
+        builder.add_node("product_agent", self._node("product_agent", self.product_agent))
+        builder.add_node("orders_agent", self._node("orders_agent", self.orders_agent))
+        builder.add_node("support_agent", self._node("support_agent", self.support_agent))
+        builder.add_node("handoff", self._node("handoff", self.handoff))
+        builder.add_node("human_handoff", self._node("human_handoff", self.human_handoff))
+        builder.add_node("end_session", self._node("end_session", self.end_session))
+        builder.add_node("supervisor_agent", self._node("supervisor_agent", self.supervisor_agent))
+        builder.add_node("output_supervisor", self._node("output_supervisor", self.output_supervisor))
+        builder.add_node("output_guardrails", self._node("output_guardrails", self.output_guardrails))
+        builder.add_node("judge", self._node("judge", self.judge))
+        builder.add_node("supervisor_review", self._node("supervisor_review", self.supervisor_review))
+        builder.add_node("persist_long_term_memory", self._node("persist_long_term_memory", self.persist_long_term_memory))
+        builder.add_node("persist", self._node("persist", self.persist))
+
+        builder.add_edge(START, "input_guardrails")
+        builder.add_conditional_edges(
+            "input_guardrails",
+            self._after_input_guardrails,
+            {"blocked": "persist", "continue": "load_long_term_memory"},
+        )
+        builder.add_edge("load_long_term_memory", "routing_decision")
+        builder.add_conditional_edges(
+            "routing_decision",
+            lambda s: s.get("route", "billing_agent"),
+            {
+                "billing_agent": "billing_agent",
+                "product_agent": "product_agent",
+                "orders_agent": "orders_agent",
+                "support_agent": "support_agent",
+                "handoff": "handoff",
+                "human_handoff": "human_handoff",
+                "end_session": "end_session",
+                "supervisor_agent": "supervisor_agent",
+            },
+        )
+        builder.add_edge("billing_agent", "output_supervisor")
+        builder.add_edge("product_agent", "output_supervisor")
+        builder.add_edge("orders_agent", "output_supervisor")
+        builder.add_edge("support_agent", "output_supervisor")
+        builder.add_edge("handoff", "output_supervisor")
+        builder.add_edge("human_handoff", "output_supervisor")
+        builder.add_edge("end_session", "output_supervisor")
+        builder.add_edge("supervisor_agent", "output_supervisor")
+        builder.add_edge("output_supervisor", "output_guardrails")
+        builder.add_edge("output_guardrails", "judge")
+        builder.add_edge("judge", "supervisor_review")
+        builder.add_edge("supervisor_review", "persist_long_term_memory")
+        builder.add_edge("persist_long_term_memory", "persist")
+        builder.add_edge("persist", END)
+
+        return builder.compile(checkpointer=create_langgraph_checkpointer(self.settings))
+
+    def _after_input_guardrails(self, state):
+        return "blocked" if state.get("blocked") else "continue"
+
+    async def input_guardrails(self, state):
+        if state.get("session_ended") is True:
+            answer = str(getattr(
+                self.settings,
+                "SESSION_ALREADY_ENDED_MESSAGE",
+                "Este atendimento já foi encerrado. Inicie uma nova sessão para continuar.",
+            ))
+            await self.telemetry.event(
+                "session.message.rejected_after_end",
+                {"session_id": state.get("conversation_key") or state.get("session_id")},
+            )
+            return {
+                "answer": answer,
+                "final_answer": answer,
+                "blocked": True,
+                "session_control": "END_SESSION",
+                "session_ended": True,
+                "next_state": "SESSION_ENDED",
+            }
+        async with self.telemetry.span(
+            "workflow.input_guardrails",
+            session_id=state.get("conversation_key") or state.get("session_id"),
+            input=state.get("user_text"),
+        ):
+            history_texts = [m.get("content", "") for m in state.get("history", [])]
+            await self.observer.emit_grl(
+                "001",
+                {
+                    "session_id": state.get("conversation_key") or state.get("session_id"),
+                    "tenant_id": state.get("tenant_id"),
+                    "agent_id": state.get("agent_id"),
+                    "phase": "input",
+                },
+                component="workflow.input_guardrails.start",
+            )
+            sanitized, decisions = await self.guardrails.run_input(
+                state["user_text"],
+                {
+                    **(state.get("context") or {}),
+                    "history_texts": history_texts,
+                    "tenant_id": state.get("tenant_id"),
+                    "agent_id": state.get("agent_id"),
+                    "agent_profile": state.get("agent_profile") or {},
+                },
+            )
+            for _decision in decisions:
+                await self.guardrail_telemetry.evaluated("input", _decision)
+                await self.observer.emit_grl(
+                    "002" if _decision.allowed else "004",
+                    {
+                        "session_id": state.get("conversation_key") or state.get("session_id"),
+                        "tenant_id": state.get("tenant_id"),
+                        "agent_id": state.get("agent_id"),
+                        "phase": "input",
+                        "rail_code": getattr(_decision, "code", None),
+                        "allowed": bool(_decision.allowed),
+                        "reason": getattr(_decision, "reason", None),
+                    },
+                    component="workflow.input_guardrails.decision",
+                )
+                if not _decision.allowed:
+                    await self.guardrail_telemetry.blocked("input", _decision)
+            await self.telemetry.event(
+                "guardrails.input.completed",
+                {
+                    "session_id": state.get("conversation_key") or state.get("session_id"),
+                    "tenant_id": state.get("tenant_id"),
+                    "agent_id": state.get("agent_id"),
+                    "decisions": [d.model_dump() for d in decisions],
+                },
+            )
+            await self.observer.emit_grl(
+                "009",
+                {
+                    "session_id": state.get("conversation_key") or state.get("session_id"),
+                    "tenant_id": state.get("tenant_id"),
+                    "agent_id": state.get("agent_id"),
+                    "phase": "input",
+                    "blocked": any(not d.allowed for d in decisions),
+                    "decision_count": len(decisions),
+                },
+                component="workflow.input_guardrails.final",
+            )
+            if any(not d.allowed for d in decisions):
+                return {
+                    "sanitized_input": sanitized,
+                    "answer": "Não consegui seguir com essa mensagem por regra de segurança.",
+                    "final_answer": "Não consegui seguir com essa mensagem por regra de segurança.",
+                    "guardrail_decisions": [d.model_dump() for d in decisions],
+                    "route": "blocked",
+                    "blocked": True,
+                }
+            return {
+                "sanitized_input": sanitized,
+                "guardrail_decisions": [d.model_dump() for d in decisions],
+                "blocked": False,
+            }
+
+    async def routing_decision(self, state):
+        mode = getattr(self.settings, "ROUTING_MODE", "router")
+        async with self.telemetry.span(
+            "workflow.routing_decision",
+            session_id=state.get("conversation_key") or state.get("session_id"),
+            input={
+                "mode": mode,
+                "text": state.get("sanitized_input") or state.get("user_text"),
+                "previous_state": state.get("next_state"),
+            },
+        ):
+            if mode == "supervisor":
+                plan = await self.supervisor.route_plan(state)
+                await self.langgraph_telemetry.edge("routing_decision", "supervisor_agent", state, {"method": "supervisor", "intent": plan.intent, "confidence": plan.confidence})
+                return {
+                    "route": "supervisor_agent",
+                    "intent": plan.intent,
+                    "supervisor_plan": {
+                        "agents": plan.agents,
+                        "intent": plan.intent,
+                        "confidence": plan.confidence,
+                        "reason": plan.reason,
+                        "metadata": plan.metadata,
+                    },
+                    "route_decision": {
+                        "route": "supervisor_agent",
+                        "agent": "supervisor",
+                        "intent": plan.intent,
+                        "confidence": plan.confidence,
+                        "reason": plan.reason,
+                        "method": "supervisor",
+                        "metadata": plan.metadata,
+                    },
+                }
+
+            decision = await self.router.route(state)
+            await self.langgraph_telemetry.edge("routing_decision", decision.route, state, {"method": getattr(decision, "method", None), "intent": decision.intent, "confidence": decision.confidence})
+            await self.observer.emit_ic(
+                "ROUTE_SELECTED",
+                {
+                    "session_id": state.get("conversation_key") or state.get("session_id"),
+                    "tenant_id": state.get("tenant_id"),
+                    "agent_id": state.get("agent_id"),
+                    "route": decision.route,
+                    "intent": decision.intent,
+                    "confidence": decision.confidence,
+                    "method": getattr(decision, "method", None),
+                },
+                component="workflow.routing_decision",
+            )
+            return {
+                "route": decision.route,
+                "intent": decision.intent,
+                "route_decision": decision.model_dump(mode="json"),
+                "domain": decision.domain,
+                "mcp_tools": decision.mcp_tools,
+                "next_state": decision.next_state,
+                "active_agent": decision.agent,
+                "route_bypassed": decision.method == "continuity",
+                "session_control": (decision.metadata or {}).get("session_control", ""),
+                "human_handoff_requested": (decision.metadata or {}).get("session_control") == "HUMAN_HANDOFF",
+                "session_ended": (decision.metadata or {}).get("session_control") == "END_SESSION",
+                "continuity_signal": {
+                    "decision": (decision.metadata or {}).get("continuity_decision"),
+                    "confidence": decision.confidence if decision.method == "continuity" else None,
+                    "reason": decision.reason if decision.method == "continuity" else None,
+                    "profile": (decision.metadata or {}).get("continuity_profile"),
+                } if decision.method == "continuity" else {},
+            }
+
+    async def billing_agent(self, state):
+        async with self.telemetry.span(
+            "workflow.agent.billing",
+            session_id=state.get("conversation_key") or state.get("session_id"),
+            input={"intent": state.get("intent")},
+        ):
+            return await self.billing.run(state)
+
+    async def product_agent(self, state):
+        async with self.telemetry.span(
+            "workflow.agent.product",
+            session_id=state.get("conversation_key") or state.get("session_id"),
+            input={"intent": state.get("intent")},
+        ):
+            return await self.product.run(state)
+
+    async def orders_agent(self, state):
+        async with self.telemetry.span(
+            "workflow.agent.orders",
+            session_id=state.get("conversation_key") or state.get("session_id"),
+            input={"intent": state.get("intent")},
+        ):
+            return await self.orders.run(state)
+
+    async def support_agent(self, state):
+        async with self.telemetry.span(
+            "workflow.agent.support",
+            session_id=state.get("conversation_key") or state.get("session_id"),
+            input={"intent": state.get("intent")},
+        ):
+            return await self.support.run(state)
+
+    async def supervisor_agent(self, state):
+        """Executa um ou mais agentes no modo supervisor e consolida a resposta.
+
+        Este nó mantém o desenho de supervisor sem obrigar o restante do workflow
+        a conhecer quantos agentes foram acionados. Cada execução especializada
+        recebe o mesmo estado, mas com route/active_agent atualizados.
+        """
+        plan = state.get("supervisor_plan") or {}
+        agents = plan.get("agents") or ["billing_agent"]
+        handlers = {
+            "billing_agent": self.billing.run,
+            "product_agent": self.product.run,
+            "orders_agent": self.orders.run,
+            "support_agent": self.support.run,
+        }
+        partials = []
+        mcp_results = []
+        async with self.telemetry.span(
+            "workflow.supervisor_agent",
+            session_id=state.get("conversation_key") or state.get("session_id"),
+            input={"agents": agents, "intent": state.get("intent")},
+        ):
+            for agent_name in agents:
+                handler = handlers.get(agent_name)
+                if handler is None:
+                    continue
+                child_state = {**state, "route": agent_name, "active_agent": agent_name}
+                result = await handler(child_state)
+                partials.append({"agent": agent_name, "answer": result.get("answer", "")})
+                mcp_results.extend(result.get("mcp_results") or [])
+
+        if len(partials) == 1:
+            answer = partials[0]["answer"]
+        else:
+            joined = "\n\n".join(f"{p['agent']}: {p['answer']}" for p in partials)
+            answer = (
+                "[Supervisor] Consolidação de múltiplos agentes acionados.\n"
+                f"{joined}"
+            )
+        return {
+            "answer": answer,
+            "supervisor_results": partials,
+            "mcp_results": mcp_results,
+            "next_state": "SUPERVISOR_ACTIVE",
+        }
+
+    async def handoff(self, state):
+        async with self.telemetry.span("workflow.handoff", session_id=state.get("session_id")):
+            target = (state.get("route_decision") or {}).get("metadata", {}).get("target_agent")
+            answer = (
+                "Vou redirecionar sua solicitação para o especialista correto. "
+                f"Destino sugerido: {target or 'agente especializado'}."
+            )
+            return {"answer": answer}
+
+    async def human_handoff(self, state):
+        session_id = state.get("conversation_key") or state.get("session_id")
+        async with self.telemetry.span("workflow.human_handoff", session_id=session_id):
+            answer = str(getattr(self.settings, "HUMAN_HANDOFF_MESSAGE", "Vou encaminhar seu atendimento para uma pessoa."))
+            await self.telemetry.event(
+                "session.human_handoff.requested",
+                {
+                    "session_id": session_id,
+                    "tenant_id": state.get("tenant_id"),
+                    "agent_id": state.get("agent_id"),
+                    "reason": (state.get("route_decision") or {}).get("reason"),
+                },
+            )
+            return {
+                "answer": answer,
+                "session_control": "HUMAN_HANDOFF",
+                "human_handoff_requested": True,
+                "session_ended": False,
+                "next_state": "HUMAN_HANDOFF_REQUESTED",
+            }
+
+    async def end_session(self, state):
+        session_id = state.get("conversation_key") or state.get("session_id")
+        async with self.telemetry.span("workflow.end_session", session_id=session_id):
+            answer = str(getattr(self.settings, "END_SESSION_MESSAGE", "Atendimento encerrado. Obrigado pelo contato."))
+            await self.telemetry.event(
+                "session.end.requested",
+                {
+                    "session_id": session_id,
+                    "tenant_id": state.get("tenant_id"),
+                    "agent_id": state.get("agent_id"),
+                    "reason": (state.get("route_decision") or {}).get("reason"),
+                },
+            )
+            return {
+                "answer": answer,
+                "session_control": "END_SESSION",
+                "session_ended": True,
+                "human_handoff_requested": False,
+                "next_state": "SESSION_ENDED",
+            }
+
+    async def output_supervisor(self, state):
+        """Valida a resposta candidata com o OutputSupervisor corporativo.
+
+        Este nó não substitui o roteador/supervisor multiagente. Ele roda após o
+        agente gerar `answer` e antes dos judges/persistência, produzindo campos
+        supervisor_* no state e eventos GRL.001..GRL.009 via AgentObserver.
+        """
+        if not bool(getattr(self.settings, "ENABLE_OUTPUT_SUPERVISOR", True)):
+            return {
+                "output_guardrails_already_applied": False,
+                "supervisor_action": "disabled",
+                "supervisor_attempt": int(state.get("supervisor_attempt", 0)),
+            }
+
+        candidate = state.get("answer") or ""
+        context = {
+            **(state.get("context") or {}),
+            "tenant_id": state.get("tenant_id"),
+            "agent_id": state.get("agent_id"),
+            "session_id": state.get("conversation_key") or state.get("session_id"),
+            "route": state.get("route"),
+            "intent": state.get("intent"),
+            "supervisor_attempt": int(state.get("supervisor_attempt", 0)),
+        }
+        async with self.telemetry.span(
+            "workflow.output_supervisor",
+            session_id=state.get("conversation_key") or state.get("session_id"),
+            input=candidate,
+        ):
+            decision = await self.output_supervisor_engine.evaluate(candidate, context)
+            action = decision.action.value
+            await self.telemetry.event(
+                "output_supervisor.completed",
+                {
+                    "session_id": context["session_id"],
+                    "tenant_id": state.get("tenant_id"),
+                    "agent_id": state.get("agent_id"),
+                    "action": action,
+                    "approved": decision.approved,
+                    "guidance": decision.guidance,
+                },
+            )
+
+            await self.observer.emit_ic(
+                "IC.OUTPUT_SUPERVISOR_COMPLETED",
+                {
+                    "session_id": context["session_id"],
+                    "tenant_id": state.get("tenant_id"),
+                    "agent_id": state.get("agent_id"),
+                    "route": state.get("route"),
+                    "intent": state.get("intent"),
+                    "action": action,
+                    "approved": decision.approved,
+                    "result_count": len(decision.results),
+                },
+                component="workflow.output_supervisor",
+            )
+
+            if decision.action in {RailAction.ALLOW, RailAction.SANITIZE, RailAction.OBSERVE}:
+                final_answer = decision.candidate
+            elif decision.action == RailAction.HANDOVER:
+                final_answer = "Vou encaminhar seu atendimento para continuidade com um especialista."
+            else:
+                final_answer = decision.fallback_message
+
+            return {
+                "answer": final_answer,
+                "final_answer": final_answer,
+                "supervisor_action": action,
+                "supervisor_guidance": decision.guidance,
+                "supervisor_attempt": int(state.get("supervisor_attempt", 0)) + (1 if decision.action == RailAction.RETRY else 0),
+                "supervisor_handover_reason": decision.handover_reason,
+                "output_supervisor_results": [
+                    {
+                        "code": r.code,
+                        "action": r.action.value,
+                        "reason": r.reason,
+                        "guidance": r.guidance,
+                        "metadata": r.metadata,
+                    }
+                    for r in decision.results
+                ],
+                "output_guardrails_already_applied": True,
+                "guardrail_decisions": state.get("guardrail_decisions", [])
+                + [item for r in decision.results for item in (r.metadata or {}).get("legacy_decisions", [])],
+            }
+
+    async def output_guardrails(self, state):
+        if state.get("output_guardrails_already_applied"):
+            return {"final_answer": state.get("final_answer") or state.get("answer") or ""}
+
+        async with self.telemetry.span(
+            "workflow.output_guardrails",
+            session_id=state.get("conversation_key") or state.get("session_id"),
+            input=state.get("answer"),
+        ):
+            await self.observer.emit_grl(
+                "001",
+                {
+                    "session_id": state.get("conversation_key") or state.get("session_id"),
+                    "tenant_id": state.get("tenant_id"),
+                    "agent_id": state.get("agent_id"),
+                    "phase": "output",
+                    "route": state.get("route"),
+                    "intent": state.get("intent"),
+                },
+                component="workflow.output_guardrails.start",
+            )
+            final, decisions = await self.guardrails.run_output(
+                state["answer"], state.get("context", {})
+            )
+            for _decision in decisions:
+                await self.guardrail_telemetry.evaluated("output", _decision)
+                await self.observer.emit_grl(
+                    "002" if _decision.allowed else "004",
+                    {
+                        "session_id": state.get("conversation_key") or state.get("session_id"),
+                        "tenant_id": state.get("tenant_id"),
+                        "agent_id": state.get("agent_id"),
+                        "phase": "output",
+                        "rail_code": getattr(_decision, "code", None),
+                        "allowed": bool(_decision.allowed),
+                        "reason": getattr(_decision, "reason", None),
+                    },
+                    component="workflow.output_guardrails.decision",
+                )
+                if not _decision.allowed:
+                    await self.guardrail_telemetry.blocked("output", _decision)
+            await self.telemetry.event(
+                "guardrails.output.completed",
+                {
+                    "session_id": state.get("conversation_key") or state.get("session_id"),
+                    "tenant_id": state.get("tenant_id"),
+                    "agent_id": state.get("agent_id"),
+                    "decisions": [d.model_dump() for d in decisions],
+                },
+            )
+            await self.observer.emit_grl(
+                "009",
+                {
+                    "session_id": state.get("conversation_key") or state.get("session_id"),
+                    "tenant_id": state.get("tenant_id"),
+                    "agent_id": state.get("agent_id"),
+                    "phase": "output",
+                    "blocked": any(not d.allowed for d in decisions),
+                    "decision_count": len(decisions),
+                },
+                component="workflow.output_guardrails.final",
+            )
+            return {
+                "final_answer": final,
+                "guardrail_decisions": state.get("guardrail_decisions", [])
+                + [d.model_dump() for d in decisions],
+            }
+
+    async def judge(self, state):
+        async with self.telemetry.span(
+            "workflow.judge",
+            session_id=state.get("conversation_key") or state.get("session_id"),
+            input={"question": state.get("user_text"), "answer": state.get("final_answer")},
+        ):
+            judge_context = dict(state.get("context", {}) or {})
+            judge_context["mcp_results"] = state.get("mcp_results", [])
+            relevant_transaction_evidence = list(state.get("relevant_transaction_evidence") or [])
+            judge_context["transaction_evidence"] = relevant_transaction_evidence
+            current_evidence = list(state.get("mcp_results", []) or [])
+            current_evidence.extend(relevant_transaction_evidence)
+            judge_context["evidence"] = current_evidence or judge_context.get("evidence")
+            judge_context["route"] = state.get("route")
+            judge_context["intent"] = state.get("intent")
+            # Judge sampling must see the finalized transaction state. These
+            # fields are populated by the agent/tool runtime before this node.
+            for key in (
+                "transaction_status",
+                "confirmation_required",
+                "confirmation_received",
+                "tool_policy_result",
+                "selected_tool_call",
+                "pending_tool_call",
+            ):
+                judge_context[key] = state.get(key)
+            judge_context["transactional_tools"] = [
+                result.get("tool_name")
+                for result in state.get("mcp_results", [])
+                if isinstance(result, dict)
+                and (
+                    (result.get("metadata") or {}).get("operation_type") == "transactional"
+                    or result.get("awaiting_confirmation")
+                    or result.get("transaction_status")
+                )
+            ]
+            results = await self.judges.evaluate_all(
+                state["user_text"], state["final_answer"], judge_context
+            )
+            for _result in results:
+                await self.judge_telemetry.evaluated(_result)
+            await self.telemetry.event(
+                "judges.completed",
+                {
+                    "session_id": state.get("conversation_key") or state.get("session_id"),
+                    "tenant_id": state.get("tenant_id"),
+                    "agent_id": state.get("agent_id"),
+                    "results": [r.model_dump() for r in results],
+                },
+            )
+            return {"judge_results": [r.model_dump() for r in results]}
+
+    async def supervisor_review(self, state):
+        async with self.telemetry.span(
+            "workflow.supervisor_review",
+            session_id=state.get("conversation_key") or state.get("session_id"),
+            input=state.get("final_answer"),
+        ):
+            ok, answer = await self.supervisor.review(
+                state["final_answer"], state.get("context", {})
+            )
+            await self.telemetry.event(
+                "supervisor.review.completed",
+                {"session_id": state.get("session_id"), "approved": ok},
+            )
+            return {"final_answer": answer if ok else answer}
+
+    async def load_long_term_memory(self, state):
+        """Carrega LTM antes do roteamento e mantém o resultado no estado.
+
+        A carga explícita evita depender apenas do agente selecionado para realizar
+        a recuperação e facilita o diagnóstico de identidade/namespace.
+        """
+        try:
+            memories = await self.long_term_memory_manager.load(state)
+            serialized = []
+            context_lines = []
+            for item in memories or []:
+                if hasattr(item, "model_dump"):
+                    data = item.model_dump(mode="json")
+                elif hasattr(item, "__dict__"):
+                    data = dict(item.__dict__)
+                elif isinstance(item, dict):
+                    data = dict(item)
+                else:
+                    data = {"value": str(item)}
+                serialized.append(data)
+                key = data.get("key") or data.get("memory_key") or data.get("category") or "memory"
+                value = data.get("value") or data.get("memory_value")
+                if value not in (None, ""):
+                    context_lines.append(f"- {key}: {value}")
+
+            return {
+                "long_term_memories": serialized,
+                "long_term_memory_context": "\n".join(context_lines),
+            }
+        except Exception as exc:
+            await self.telemetry.event(
+                "long_term_memory.load.failed",
+                {
+                    "session_id": state.get("conversation_key") or state.get("session_id"),
+                    "tenant_id": state.get("tenant_id"),
+                    "agent_id": state.get("agent_id"),
+                    "subject_key": state.get("long_term_memory_subject_key"),
+                    "error": str(exc),
+                },
+            )
+            return {
+                "long_term_memories": [],
+                "long_term_memory_context": "",
+                "long_term_memory_load_error": str(exc),
+            }
+
+    async def persist_long_term_memory(self, state):
+        try:
+            result = await self.long_term_memory_manager.persist_turn(state)
+            await self.telemetry.event(
+                "long_term_memory.persist.completed",
+                {
+                    "session_id": state.get("conversation_key") or state.get("session_id"),
+                    "tenant_id": state.get("tenant_id"),
+                    "agent_id": state.get("agent_id"),
+                    "subject_key": state.get("long_term_memory_subject_key"),
+                    "result": result,
+                },
+            )
+            return {"long_term_memory_write_result": result}
+        except Exception as exc:
+            await self.telemetry.event(
+                "long_term_memory.persist.failed",
+                {
+                    "session_id": state.get("conversation_key") or state.get("session_id"),
+                    "tenant_id": state.get("tenant_id"),
+                    "agent_id": state.get("agent_id"),
+                    "subject_key": state.get("long_term_memory_subject_key"),
+                    "error": str(exc),
+                },
+            )
+            return {"long_term_memory_write_result": {"saved": 0, "error": str(exc)}}
+
+    async def persist(self, state):
+        async with self.telemetry.span(
+            "workflow.persist",
+            session_id=state.get("conversation_key") or state.get("session_id"),
+            input={"route": state.get("route"), "intent": state.get("intent")},
+        ):
+            await self.observer.emit_ic(
+                "AGENT_COMPLETED",
+                {
+                    "session_id": state.get("conversation_key") or state["session_id"],
+                    "tenant_id": state.get("tenant_id"),
+                    "agent_id": state.get("agent_id"),
+                    "route": state.get("route"),
+                    "intent": state.get("intent"),
+                    "route_decision": state.get("route_decision"),
+                    "judges": state.get("judge_results", []),
+                    "mcp_tools": state.get("mcp_tools", []),
+                    "mcp_results": state.get("mcp_results", []),
+                    "transaction_evidence": state.get("relevant_transaction_evidence", []),
+                },
+            )
+
+            await self.observer.emit_noc(
+                "006",
+                {
+                    "session_id": state.get("conversation_key") or state["session_id"],
+                    "tenant_id": state.get("tenant_id"),
+                    "agent_id": state.get("agent_id"),
+                    "route": state.get("route"),
+                    "intent": state.get("intent"),
+                    "answer_chars": len(state.get("final_answer") or ""),
+                },
+                component="workflow.persist",
+            )
+
+            await self.telemetry.event(
+                "agent.completed",
+                {
+                    "session_id": state.get("conversation_key") or state["session_id"],
+                    "tenant_id": state.get("tenant_id"),
+                    "agent_id": state.get("agent_id"),
+                    "route": state.get("route"),
+                    "intent": state.get("intent"),
+                    "answer_chars": len(state.get("final_answer") or ""),
+                },
+            )
+            return state
+
+    async def ainvoke(self, state):
+        thread_id = state.get("conversation_key") or state["session_id"]
+        config = {"configurable": {"thread_id": thread_id}}
+        async with self.telemetry.span(
+            "workflow.langgraph.ainvoke",
+            session_id=state.get("conversation_key") or state.get("session_id"),
+            user_id=state.get("context", {}).get("user_id"),
+            input={"user_text": state.get("user_text")},
+            tags=["langgraph", "agent-workflow", f"routing-mode:{getattr(self.settings, 'ROUTING_MODE', 'router')}",],
+        ):
+            await self.workflow_telemetry.started("agent_workflow", state)
+            await self.observer.emit_noc(
+                "001",
+                {
+                    "session_id": state.get("conversation_key") or state.get("session_id"),
+                    "tenant_id": state.get("tenant_id"),
+                    "agent_id": state.get("agent_id"),
+                    "channel_id": (state.get("context") or {}).get("channel"),
+                    "message_id": (state.get("context") or {}).get("message_id"),
+                    "ura_call_id": (state.get("context") or {}).get("ura_call_id"),
+                },
+                component="workflow.ainvoke",
+            )
+            await self.observer.emit_ic(
+                "AGENT_STARTED",
+                {
+                    "session_id": state.get("conversation_key") or state.get("session_id"),
+                    "tenant_id": state.get("tenant_id"),
+                    "agent_id": state.get("agent_id"),
+                    "channel_id": (state.get("context") or {}).get("channel"),
+                    "message_id": (state.get("context") or {}).get("message_id"),
+                    "user_text_chars": len(state.get("user_text") or ""),
+                },
+                component="workflow.ainvoke",
+            )
+            try:
+                result = await self.graph.ainvoke(state, config=config)
+                await self.workflow_telemetry.completed("agent_workflow", result)
+                return result
+            except Exception as exc:
+                await self.workflow_telemetry.failed("agent_workflow", exc)
+                await self.observer.emit_noc(
+                    "005",
+                    {
+                        "session_id": state.get("conversation_key") or state.get("session_id"),
+                        "tenant_id": state.get("tenant_id"),
+                        "agent_id": state.get("agent_id"),
+                        "error": str(exc),
+                        "exception_type": exc.__class__.__name__,
+                    },
+                    component="workflow.ainvoke",
+                )
+                raise

Tuning-Performance/Transaction_Evidence/agent_template_backend/config/agents.yaml

@@ -0,0 +1,33 @@
+default_agent_id: telecom_contas
+agents:
+  - agent_id: telecom_contas
+    name: Agente Telecom Contas
+    description: Template de atendimento para faturas, produtos e suporte de telecom.
+    prompt_policy_path: ./config/agents/telecom_contas/prompt_policy.yaml
+    routing_config_path: ./config/routing.yaml
+    guardrails_config_path: ./config/agents/telecom_contas/guardrails.yaml
+    judges_config_path: ./config/agents/telecom_contas/judges.yaml
+    mcp_servers_config_path: ./config/mcp_servers.yaml
+    tools_config_path: ./config/tools.yaml
+    metadata:
+      domain: telecom
+      system_prefix: |
+        Você está executando o agent_template telecom_contas.
+        Use somente políticas, memória, checkpoints, guardrails e judges deste agent_id.
+        Não misture histórico ou decisões de outros agentes.
+
+  - agent_id: retail_orders
+    name: Agente Retail Pedidos
+    description: Template de varejo para pedidos, produtos, troca/devolução e garantia.
+    prompt_policy_path: ./config/agents/retail_orders/prompt_policy.yaml
+    routing_config_path: ./config/routing.yaml
+    guardrails_config_path: ./config/agents/retail_orders/guardrails.yaml
+    judges_config_path: ./config/agents/retail_orders/judges.yaml
+    mcp_servers_config_path: ./config/mcp_servers.yaml
+    tools_config_path: ./config/tools.yaml
+    metadata:
+      domain: retail
+      system_prefix: |
+        Você está executando o agent_template retail_orders.
+        Use somente políticas, memória, checkpoints, guardrails e judges deste agent_id.
+        Não misture histórico ou decisões de outros agentes.