new feature: transaction evidence

Tuning-Performance/README.md

@@ -6,3 +6,4 @@ 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.

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.

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

@@ -0,0 +1,8 @@
+input:
+  - code: MSK
+    enabled: true
+  - code: VLOOP
+    enabled: true
+output:
+  - code: REVPREC
+    enabled: true

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

@@ -0,0 +1,7 @@
+judges:
+  - name: response_quality
+    enabled: true
+    threshold: 0.7
+  - name: groundedness
+    enabled: true
+    threshold: 0.6

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

@@ -0,0 +1,6 @@
+id: retail_orders_prompt_policy
+version: 1
+description: Prompt base isolado do agente de varejo/pedidos.
+system_prefix: |
+  Você é um agente corporativo de varejo especializado em pedidos, entrega, troca, devolução e garantia.
+  Seja claro, objetivo e não use regras de negócio de telecom neste agente.

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

@@ -0,0 +1,8 @@
+input:
+  - code: MSK
+    enabled: true
+  - code: VLOOP
+    enabled: true
+output:
+  - code: REVPREC
+    enabled: true

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

@@ -0,0 +1,20 @@
+enabled: true
+fail_closed: true
+profile: judge
+
+judges:
+  - name: response_quality
+    enabled: true
+    threshold: 0.7
+
+  - name: groundedness
+    enabled: true
+    threshold: 0.6
+
+  - name: sentiment
+    enabled: true
+    fail_on_negative: false
+
+  - name: tone
+    enabled: true
+    fail_closed: true

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

@@ -0,0 +1,6 @@
+id: telecom_contas_prompt_policy
+version: 1
+description: Prompt base isolado do agente de telecom/contas.
+system_prefix: |
+  Você é um agente corporativo de atendimento telecom especializado em faturas, produtos, VAS e suporte.
+  Seja claro, objetivo e não prometa execução operacional sem ferramenta ou confirmação válida.

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

@@ -0,0 +1,12 @@
+input:
+  - code: MSK
+    enabled: true
+  - code: VLOOP
+    enabled: true
+output:
+  - code: REVPREC
+    enabled: true
+  - code: PINJ
+    enabled: true
+  - code: DLEX_OUT
+    enabled: true

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

@@ -0,0 +1,55 @@
+identity:
+  version: "2"
+  required:
+    - session_key
+  keys:
+    customer_key:
+      description: Cliente/assinante/consumidor canônico.
+      sources:
+        - business_context.customer_key
+        - customer_key
+        - msisdn
+        - customer_id
+        - user_id
+        - ani
+        - from
+    contract_key:
+      description: Contrato, conta, fatura, pedido ou asset principal.
+      sources:
+        - business_context.contract_key
+        - contract_key
+        - invoice_id
+        - current_invoice_number
+        - order_id
+        - pedido_id
+        - asset_id
+    interaction_key:
+      description: Chave externa da interação/call/chat vinda do canal.
+      sources:
+        - business_context.interaction_key
+        - interaction_key
+        - ura_call_id
+        - call_id
+        - message_id
+    account_key:
+      description: Conta de cobrança/conta comercial.
+      sources:
+        - business_context.account_key
+        - account_key
+        - account_id
+        - billing_account_id
+    resource_key:
+      description: Recurso/linha/produto/asset específico.
+      sources:
+        - business_context.resource_key
+        - resource_key
+        - asset_id
+        - product_id
+        - sku
+    session_key:
+      description: Sessão técnica estável já escopada por tenant e agente.
+      sources:
+        - business_context.session_key
+        - session_key
+        - conversation_key
+        - session_id

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

@@ -0,0 +1,18 @@
+enabled: true
+fail_closed: true
+profile: judge
+judges:
+- name: response_quality
+  enabled: true
+  threshold: 0.7
+- name: groundedness
+  enabled: true
+  threshold: 0.6
+- name: sentiment
+  enabled: true
+  fail_on_negative: false
+- name: tone
+  enabled: true
+  fail_closed: true
+sample_rate: 0.25
+always_run_for_transactional: true

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

@@ -0,0 +1,104 @@
+mcp_parameter_mapping:
+  defaults:
+    use_mock: true
+  tools:
+    consultar_fatura:
+      map:
+        customer_key: msisdn
+        contract_key: invoice_id
+        interaction_key: ura_call_id
+        session_key: session_id
+      extract:
+        mes_referencia:
+          from: message
+          type: int
+          strategy: month_name_pt
+          description: 'Extrair mês citado na mensagem. janeiro=1, fevereiro=2, março=3,
+            abril=4, maio=5, junho=6, julho=7, agosto=8, setembro=9, outubro=10, novembro=11,
+            dezembro=12.
+
+            '
+    consultar_pagamentos:
+      map:
+        customer_key: msisdn
+        interaction_key: ura_call_id
+        session_key: session_id
+    consultar_plano:
+      map:
+        customer_key: msisdn
+        resource_key: asset_id
+        contract_key: asset_id
+        session_key: session_id
+    listar_servicos:
+      map:
+        customer_key: msisdn
+        session_key: session_id
+    consultar_pedido:
+      map:
+        customer_key: customer_id
+        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
+    consultar_entrega:
+      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
+    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
+      defaults:
+        reason: Solicitação aberta pelo atendimento conversacional.
+      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_devolucao:
+      map:
+        session_key: session_id
+      defaults:
+        reason: Solicitação aberta pelo atendimento conversacional.
+      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

Tuning-Performance/Transaction_Evidence/agent_template_backend/config/mcp_servers.docker.yaml

@@ -0,0 +1,12 @@
+servers:
+  telecom:
+    transport: http
+    endpoint: http://telecom-mcp:8100/mcp
+    enabled: true
+    description: MCP Server Telecom via docker-compose.
+
+  retail:
+    transport: http
+    endpoint: http://retail-mcp:8200/mcp
+    enabled: true
+    description: MCP Server Retail via docker-compose.

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

@@ -0,0 +1,30 @@
+# MCP servers registry.
+# transport=http keeps the legacy framework mock contract:
+#   GET  <endpoint>/tools/list
+#   POST <endpoint>/tools/call
+# transport=fastmcp uses official MCP Streamable HTTP, typically endpoint http://host:port/mcp
+# transport=sse uses official MCP SSE, typically endpoint http://host:port/sse
+servers:
+  #  telecom:
+  #    enabled: true
+  #    transport: fastmcp
+  #    endpoint: http://localhost:8001/mcp
+  #    description: Telecom FastMCP server using official MCP protocol
+  #
+  #  retail:
+  #    enabled: true
+  #    transport: fastmcp
+  #    endpoint: http://localhost:8002/mcp
+  #    description: Retail FastMCP server using official MCP protocol
+
+  telecom:
+    enabled: true
+    transport: http
+    endpoint: http://localhost:8100/mcp
+    description: Telecom legacy HTTP mock MCP server
+
+  retail:
+    enabled: true
+    transport: http
+    endpoint: http://localhost:8200/mcp
+    description: Retail legacy HTTP mock MCP server

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

@@ -0,0 +1,19 @@
+tone:
+  style: "claro, objetivo, empático"
+  forbidden_phrases:
+    - "procure atendimento humano"
+vocabulary:
+  preferred:
+    fatura: "fatura"
+    contestacao: "contestação"
+intents:
+  billing_agent:
+    - fatura
+    - boleto
+    - cobrança
+    - segunda via
+  product_agent:
+    - plano
+    - produto
+    - oferta
+    - serviço

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

@@ -0,0 +1,147 @@
+# Roteamento enterprise configurável com MCP-aware intents.
+router:
+  # mode também pode ser definido por variável de ambiente ROUTING_MODE.
+  # Valores: router | supervisor
+  mode: router
+  fallback_agent: billing_agent
+  confidence_threshold: 0.65
+  allow_handoff: true
+
+state_policies:
+  - state: WAITING_BILLING_CONFIRMATION
+    agent: billing_agent
+    description: Mantém mensagens curtas como "sim" ou "não" no fluxo de fatura.
+  - state: WAITING_PRODUCT_CONFIRMATION
+    agent: product_agent
+    description: Mantém confirmações no fluxo de produtos/serviços.
+  - state: WAITING_ORDER_CONFIRMATION
+    agent: orders_agent
+    description: Mantém confirmações no fluxo de pedidos.
+  - state: WAITING_SUPPORT_CONFIRMATION
+    agent: support_agent
+    description: Mantém confirmações no fluxo de suporte retail.
+  - state: COLLECTING_BILLING_PARAMETERS
+    agent: billing_agent
+    description: Mantém a coleta de parâmetros no fluxo de faturamento.
+  - state: COLLECTING_PRODUCT_PARAMETERS
+    agent: product_agent
+    description: Mantém a coleta de parâmetros no fluxo de produtos e serviços.
+  - state: COLLECTING_ORDER_PARAMETERS
+    agent: orders_agent
+    description: Mantém a coleta de parâmetros no fluxo de pedidos.
+  - state: COLLECTING_SUPPORT_PARAMETERS
+    agent: support_agent
+    description: Mantém a coleta de parâmetros no fluxo transacional de suporte retail.
+
+intents:
+  - name: billing_invoice_explanation
+    domain: telecom
+    agent: billing_agent
+    description: Dúvidas sobre fatura, cobrança, vencimento, segunda via, contestação e valores.
+    priority: 10
+    mcp_tools:
+      - consultar_fatura
+      - consultar_pagamentos
+    keywords:
+      - fatura
+      - conta
+      - cobrança
+      - boleto
+      - vencimento
+      - segunda via
+      - contestar
+      - valor alto
+      - invoice
+    examples:
+      - Minha fatura veio alta.
+      - Quero entender uma cobrança.
+      - Preciso da segunda via da conta.
+
+  - name: product_services_information
+    domain: telecom
+    agent: product_agent
+    description: Dúvidas sobre plano, pacote, produto, serviço, VAS, internet, roaming e benefícios.
+    priority: 20
+    mcp_tools:
+      - consultar_plano
+      - listar_servicos
+    keywords:
+      - plano
+      - serviço
+      - pacote
+      - internet
+      - roaming
+      - vas
+      - benefício
+      - assinatura
+    examples:
+      - Quais serviços estão ativos no meu plano?
+      - 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
+    description: Consulta de pedido, entrega, rastreamento, atraso e status de compra.
+    priority: 30
+    mcp_tools:
+      - consultar_pedido
+      - consultar_entrega
+    keywords:
+      - pedido
+      - entrega
+      - rastreio
+      - rastreamento
+      - encomenda
+      - compra
+      - atraso
+      - correios
+    examples:
+      - Meu pedido não chegou.
+      - Quero rastrear minha entrega.
+      - Qual é o status da minha compra?
+
+  - name: retail_support_exchange_return
+    domain: retail
+    agent: support_agent
+    description: Suporte, troca, devolução, garantia e problema com produto.
+    priority: 25
+    mcp_tools:
+      - consultar_pedido
+      - solicitar_troca
+      - solicitar_devolucao
+    keywords:
+      - solicitar devolução
+      - devolver pedido
+      - solicitar troca
+      - troca
+      - devolução
+      - devolver
+      - garantia
+      - defeito
+      - produto quebrado
+      - suporte
+      - arrependimento
+    examples:
+      - Quero trocar um produto.
+      - Meu produto veio com defeito.
+      - Como faço uma devolução?

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

@@ -0,0 +1,32 @@
+version: 1
+
+# Arquivo opcional da aplicação. A ausência mantém o comportamento dos
+# templates anteriores e as políticas legadas declaradas em tools.yaml.
+defaults:
+  operation_type: read_only
+  require_confirmation: false
+
+tool_policies:
+  cancelar_pedido:
+    operation_type: transactional
+    require_confirmation: true
+    requires: [order_id]
+
+
+  solicitar_troca:
+    operation_type: transactional
+    require_confirmation: true
+
+  solicitar_devolucao:
+    operation_type: transactional
+    require_confirmation: true
+    requires: [order_id, reason]
+    execution:
+      mode: workflow
+      workflow: devolucao_pedido
+      version: active
+
+# Exemplo para uma operação real que só pode executar após confirmação:
+#  cancelar_servico:
+#    operation_type: transactional
+#    require_confirmation: true

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

@@ -0,0 +1,130 @@
+tools:
+  consultar_fatura:
+    description: Consulta dados resumidos de fatura por msisdn/invoice_id.
+    mcp_server: telecom
+    enabled: true
+    args_schema:
+      msisdn: string
+      invoice_id: string
+    selection_keywords:
+    - fatura
+    - conta
+    - boleto
+    response:
+      mode: renderer
+      renderer: telecom.invoice
+  consultar_pagamentos:
+    description: Consulta histórico de pagamentos do cliente.
+    mcp_server: telecom
+    enabled: true
+    args_schema:
+      msisdn: string
+    selection_keywords:
+    - pagamento
+    - pagamentos
+  consultar_plano:
+    description: Consulta plano ativo e atributos comerciais.
+    mcp_server: telecom
+    enabled: true
+    args_schema:
+      msisdn: string
+      asset_id: string
+    selection_keywords:
+    - plano
+    response:
+      mode: renderer
+      renderer: telecom.plan
+  listar_servicos:
+    description: Lista serviços ativos e adicionais VAS.
+    mcp_server: telecom
+    enabled: true
+    args_schema:
+      msisdn: string
+    selection_keywords:
+    - serviços
+    - servicos
+    - vas
+  consultar_pedido:
+    description: Consulta pedido de varejo por order_id/customer_id.
+    mcp_server: retail
+    enabled: true
+    args_schema:
+      order_id: string
+      customer_id: string
+    selection_keywords:
+    - consultar pedido
+    - status do pedido
+    - pedido
+    response:
+      mode: renderer
+      renderer: retail.order
+  consultar_entrega:
+    description: Consulta entrega e rastreamento do pedido.
+    mcp_server: retail
+    enabled: true
+    args_schema:
+      order_id: string
+    selection_keywords:
+    - entrega
+    - rastreio
+    - 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
+    enabled: true
+    tool_type: action
+    requires:
+    - order_id
+    - reason
+    confirmation_required: true
+    args_schema:
+      order_id: string
+      reason: string
+    selection_keywords:
+    - solicitar troca
+    - trocar
+    - troca
+    - defeito
+    - quebrado
+  solicitar_devolucao:
+    description: Simula abertura de solicitação de devolução.
+    mcp_server: retail
+    enabled: true
+    tool_type: action
+    requires:
+    - order_id
+    - reason
+    confirmation_required: true
+    args_schema:
+      order_id: string
+      reason: string
+    selection_keywords:
+    - solicitar devolução
+    - solicitar devolucao
+    - devolver pedido
+    - devolver
+    - devolução
+    - devolucao
+    - arrependimento

Tuning-Performance/Transaction_Evidence/agent_template_backend/docs/ATUALIZACAO_TEMPLATE_ANALYTICS_OUTPUT_SUPERVISOR.md

@@ -0,0 +1,95 @@
+# Atualização do Template Backend — Analytics, Observer, NOC/GRL e OutputSupervisor
+
+Esta versão do `agent_template_backend` foi atualizada para consumir as novidades transportadas para o `agent_framework`.
+
+## 1. Analytics e Pub/Sub
+
+O backend não chama mais diretamente apenas o publisher antigo de eventos. Agora ele cria um `AnalyticsPublisher`:
+
+```python
+from agent_framework.analytics.factory import create_analytics_publisher
+from agent_framework.observability.observer import AgentObserver
+
+analytics = create_analytics_publisher(settings)
+observer = AgentObserver(analytics=analytics)
+```
+
+Com isso, o mesmo backend pode publicar em:
+
+- OCI Streaming
+- GCP Pub/Sub
+- CompositePublisher, quando `ANALYTICS_PROVIDERS=oci_streaming,pubsub`
+- Noop, quando analytics estiver desligado
+
+## 2. Configuração mínima
+
+```env
+ENABLE_ANALYTICS=true
+ANALYTICS_PROVIDERS=pubsub
+GCP_PUBSUB_TOPIC_PATH=projects/<project-id>/topics/<topic-name>
+GOOGLE_APPLICATION_CREDENTIALS=/secrets/gcp-service-account.json
+```
+
+Para publicar simultaneamente em OCI Streaming e GCP Pub/Sub:
+
+```env
+ENABLE_ANALYTICS=true
+ANALYTICS_PROVIDERS=oci_streaming,pubsub
+ENABLE_OCI_STREAMING=true
+OCI_STREAM_ENDPOINT=<endpoint>
+OCI_STREAM_OCID=<stream-ocid>
+GCP_PUBSUB_TOPIC_PATH=projects/<project-id>/topics/<topic-name>
+```
+
+## 3. Observer corporativo
+
+O workflow recebeu emissão automática dos principais eventos corporativos:
+
+- `NOC.001`: início do workflow
+- `NOC.005`: exceção fatal no workflow
+- `NOC.006`: fim do workflow antes da resposta final
+- `IC.AGENT_COMPLETED`: evento informacional de conclusão
+- `GRL.001` a `GRL.009`: emitidos pelo `OutputSupervisor`
+
+## 4. OutputSupervisor
+
+Foi inserido um novo nó LangGraph:
+
+```text
+agent -> output_supervisor -> output_guardrails -> judge -> supervisor_review -> persist
+```
+
+O `OutputSupervisor` não substitui o supervisor de roteamento. Ele valida a saída candidata do agente usando o contrato corporativo:
+
+- `allow`
+- `sanitize`
+- `retry`
+- `block`
+- `handover`
+- `observe`
+
+Para compatibilidade com os guardrails já existentes, o template inclui o adapter `LegacyOutputGuardrailRail`, que converte decisões antigas `allowed=True/False` para `RailAction`.
+
+## 5. Campos adicionados ao AgentState
+
+```python
+supervisor_action: str
+supervisor_guidance: str
+supervisor_attempt: int
+supervisor_handover_reason: str
+output_supervisor_results: list[dict]
+output_guardrails_already_applied: bool
+```
+
+## 6. Arquivos alterados
+
+- `agent_template_backend/app/main.py`
+- `agent_template_backend/app/workflows/agent_graph.py`
+- `agent_template_backend/app/state.py`
+- `agent_template_backend/.env`
+- `agent_template_backend/requirements.txt`
+- `agent_framework/src/agent_framework/config/settings.py`
+
+## 7. Observação importante
+
+O `OutputSupervisor` roda os guardrails de saída por meio do adapter legado e marca `output_guardrails_already_applied=True`. Assim o nó `output_guardrails` permanece no grafo para compatibilidade, mas evita reexecutar a mesma validação quando o supervisor já aplicou os rails.

Tuning-Performance/Transaction_Evidence/agent_template_backend/docs/COMO_USAR_IC_NOC_GRL_NO_TEMPLATE.md

@@ -0,0 +1,45 @@
+# Como usar IC, NOC e GRL no Template Backend
+
+## IC — Item de Controle
+
+Use IC para registrar eventos de negócio relevantes.
+
+```python
+await observer.emit_ic(
+    "IC.FATURA_CONSULTADA",
+    {"session_id": session_id, "invoice_id": invoice_id},
+    component="billing_agent",
+)
+```
+
+## NOC — Evento operacional
+
+Use NOC para saúde técnica, latência, erros e checkpoints operacionais.
+
+```python
+await observer.emit_noc(
+    "003",
+    {"session_id": session_id, "resourceName": "ADB", "latencyMs": 120},
+    component="repository",
+)
+```
+
+## GRL — Evento de guardrail
+
+Normalmente o framework emite GRL automaticamente. Use manualmente apenas para
+rails customizados dentro do agente.
+
+```python
+await observer.emit_grl(
+    "OBSERVE",
+    {"session_id": session_id, "rail_code": "CUSTOM_POLICY"},
+    component="custom_rail",
+)
+```
+
+## Onde já existe no template
+
+- `app/workflows/agent_graph.py` emite IC/NOC no ciclo do workflow.
+- `app/agents/runtime.py` emite IC para MCP/tools.
+- `app/agents/*_agent.py` contém exemplos dentro do método `run()`.
+- `app/examples/` contém exemplos isolados.

Tuning-Performance/Transaction_Evidence/agent_template_backend/docs/CONVERSATION_SUMMARY_MEMORY_BACKEND.md

@@ -0,0 +1,48 @@
+# Backends atualizados para ConversationSummaryMemory
+
+Esta versão dos backends foi compatibilizada com a versão do framework que adiciona `ConversationSummaryMemory`.
+
+## O que mudou
+
+- `app/main.py` agora inicializa `create_conversation_summary_memory(...)` junto com `create_memory(...)`.
+- `AgentWorkflow` recebe `summary_memory` e repassa para os agentes.
+- Os agentes não montam mais prompts manuais para o LLM; agora usam `build_messages()` do framework.
+- Antes da chamada ao LLM, os agentes executam `await self.prepare_memory_context(state)`.
+- Quando habilitado por `.env`, o prompt passa a receber:
+  - resumo acumulado da conversa;
+  - últimas mensagens completas;
+  - mensagem atual;
+  - BusinessContext;
+  - MCP results;
+  - RAG context e metadata.
+
+## Configuração
+
+```env
+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
+```
+
+## Backends alterados
+
+- `backoffice_convertido_framework`
+- `agent_template_backend`
+- `agent_template_backend_day_zero`
+
+## Observação importante
+
+Estes backends esperam que o pacote `agent_framework` instalado/conectado seja a versão com os módulos:
+
+- `agent_framework.memory.summary_memory`
+- `agent_framework.memory.summary_store`
+- `AgentRuntimeMixin.prepare_memory_context()`
+- `AgentRuntimeMixin.build_messages()` com injeção de memória
+
+Use junto com o ZIP `agent_framework_conversation_summary_memory.zip`.

Tuning-Performance/Transaction_Evidence/agent_template_backend/docs/EXEMPLOS_ROUTE_HANDOFF_TRANSACOES.md

@@ -0,0 +1,14 @@
+# Exemplos implementados no template
+
+Este projeto entrega as capacidades transversais habilitadas como referência:
+
+- route stickiness semântica com o perfil `route_continuity`;
+- decisões `CONTINUE`, `ROUTE`, `HUMAN_HANDOFF` e `END_SESSION`;
+- nós globais `human_handoff` e `end_session`;
+- persistência de `active_agent`, `route_bypassed`, `continuity_signal` e controle de sessão;
+- rejeição de novas mensagens depois de `session_ended=true`;
+- políticas MCP `read_only` e `transactional` no backend;
+- exemplo `solicitar_devolucao` com `require_confirmation: true`.
+
+Para confirmar a transação, envie `confirmed: true` ou `confirmation: true` como booleano. Handoff e encerramento não chamam agentes de domínio nem MCP.
+

Tuning-Performance/Transaction_Evidence/agent_template_backend/docs/FRAMEWORK_CHANNEL_INPUT_MODE.md

@@ -0,0 +1,84 @@
+# FRAMEWORK_CHANNEL_INPUT_MODE
+
+This backend setting controls what kind of channel input the Agent Framework backend accepts.
+
+It replaces the ambiguous use of `CHANNEL_GATEWAY_MODE` inside the backend.
+
+## Values
+
+```env
+FRAMEWORK_CHANNEL_INPUT_MODE=embedded
+```
+
+The backend may use internal channel adapters to interpret simple/native channel payloads. This is useful for demos, labs, local frontend, curl tests, and simple environments.
+
+```env
+FRAMEWORK_CHANNEL_INPUT_MODE=external
+```
+
+The backend accepts only a normalized `GatewayRequest` produced by an external Channel Gateway. It does not parse native WhatsApp, Voice, Teams, or other channel payloads.
+
+## Recommended enterprise setup
+
+In the external channel gateway service:
+
+```env
+CHANNEL_GATEWAY_RUNTIME_MODE=adapter
+```
+
+In this backend:
+
+```env
+FRAMEWORK_CHANNEL_INPUT_MODE=external
+```
+
+Flow:
+
+```text
+External channel / browser / customer adapter
+  ↓
+channel_gateway:7000
+  CHANNEL_GATEWAY_RUNTIME_MODE=adapter
+  ↓ GatewayRequest
+agent_template_backend:8000
+  FRAMEWORK_CHANNEL_INPUT_MODE=external
+  ↓
+LangGraph / Agents / MCP / Guardrails
+```
+
+## Valid direct request to backend in external mode
+
+```bash
+curl -s -X POST "http://localhost:8000/gateway/message" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "channel": "web",
+    "tenant_id": "default",
+    "agent_id": "telecom_contas",
+    "payload": {
+      "message": "Quero consultar minha fatura",
+      "session_id": "backend-external-ok-001"
+    }
+  }' | jq
+```
+
+## Invalid direct request to backend in external mode
+
+```bash
+curl -i -s -X POST "http://localhost:8000/gateway/message" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "message": "Quero consultar minha fatura",
+    "session_id": "raw-payload-error-001"
+  }'
+```
+
+Expected result: HTTP 422.
+
+## Legacy compatibility
+
+`CHANNEL_GATEWAY_MODE` is still present as a legacy alias for older environments, but new deployments should use:
+
+```env
+FRAMEWORK_CHANNEL_INPUT_MODE=embedded|external
+```

Tuning-Performance/Transaction_Evidence/agent_template_backend/docs/GUARDRAILS_PARALLELOS_OBSERVER_IC.md

@@ -0,0 +1,127 @@
+# Guardrails paralelos fail-fast e Observer IC
+
+## O que foi implementado
+
+### 1. ParallelRailExecutor
+
+Arquivo principal:
+
+```text
+agent_framework/src/agent_framework/guardrails/parallel_executor.py
+```
+
+Também foi criado um alias de compatibilidade:
+
+```text
+agent_framework/src/agent_framework/guardrails/executor.py
+```
+
+Esse alias evita erro quando algum código antigo importar:
+
+```python
+from agent_framework.guardrails.executor import ParallelRailExecutor
+```
+
+### 2. Execução paralela no GuardrailPipeline
+
+Arquivo alterado:
+
+```text
+agent_framework/src/agent_framework/guardrails/pipeline.py
+```
+
+O pipeline continua retornando o contrato antigo:
+
+```python
+(texto_final, list[RailDecision])
+```
+
+mas internamente pode executar rails em paralelo com fail-fast.
+
+### 3. Execução paralela no OutputSupervisor
+
+Arquivo alterado:
+
+```text
+agent_framework/src/agent_framework/guardrails/output_supervisor.py
+```
+
+O `OutputSupervisor` agora usa `ParallelRailExecutor` quando habilitado.
+
+### 4. Configuração
+
+Novas configurações:
+
+```env
+ENABLE_PARALLEL_GUARDRAILS=true
+GUARDRAILS_FAIL_FAST=true
+```
+
+Também foram adicionadas em:
+
+```text
+agent_framework/src/agent_framework/config/settings.py
+.env
+.env.example
+agent_template_backend/.env
+agent_template_backend_day_zero/.env
+```
+
+### 5. Observer IC
+
+O `AgentObserver` já tinha `emit_ic()`.
+
+Foi complementada a API global compatível com FIRST/TIM:
+
+```python
+from agent_framework.observer import ic, aic, noc, anoc, grl, agrl
+```
+
+Exemplos:
+
+```python
+ic("AGENT_COMPLETED", data={"session_id": "..."})
+await aic("MCP_TOOL_CALLED", data={"tool_name": "consultar_fatura"})
+```
+
+### 6. ICs automáticos no template backend
+
+O backend emite agora:
+
+```text
+IC.AGENT_STARTED
+IC.ROUTE_SELECTED
+IC.MCP_TOOL_CALLED
+IC.TOOL_CALLED
+IC.AGENT_COMPLETED
+```
+
+Além dos eventos já existentes:
+
+```text
+NOC.001
+NOC.005
+NOC.006
+GRL.001 ... GRL.009
+```
+
+## Validações executadas
+
+Foram executadas validações locais com `PYTHONPATH=agent_framework/src`:
+
+```bash
+python3 -m compileall -q agent_framework/src/agent_framework agent_template_backend/app agent_template_backend_day_zero/app
+```
+
+Smoke tests executados:
+
+```text
+1. Import de ParallelRailExecutor via agent_framework.guardrails
+2. Import de ParallelRailExecutor via agent_framework.guardrails.executor
+3. Execução fail-fast: FastBlock cancela SlowAllow
+4. GuardrailPipeline paralelo retorna RailDecision legado
+5. OutputSupervisor paralelo retorna RailAction.BLOCK
+6. API global observer.ic/noc/grl/aic/anoc/agrl
+```
+
+Observação: o import completo do `agent_template_backend.app.workflows.agent_graph` depende de `langgraph`, que não está instalado no sandbox de validação. O arquivo foi validado por `compileall`, e a dependência já consta em `agent_template_backend/requirements.txt`.

Tuning-Performance/Transaction_Evidence/agent_template_backend/docs/IMPLEMENTACAO_IC_NOC_GRL_SEM_REMOVER_LOGICA.md

@@ -0,0 +1,42 @@
+# Implementação IC/NOC/GRL preservando lógica existente
+
+Esta versão mantém a lógica original dos agentes do `agent_template_backend` e adiciona observabilidade corporativa.
+
+## IC adicionados nos agentes
+
+Cada agente agora emite eventos de negócio sem alterar a resposta final:
+
+- `IC.BILLING_AGENT_STARTED` / `IC.BILLING_AGENT_COMPLETED`
+- `IC.ORDERS_AGENT_STARTED` / `IC.ORDERS_AGENT_COMPLETED`
+- `IC.PRODUCT_AGENT_STARTED` / `IC.PRODUCT_AGENT_COMPLETED`
+- `IC.SUPPORT_AGENT_STARTED` / `IC.SUPPORT_AGENT_COMPLETED`
+- `IC.<AGENT>_MCP_CONTEXT_COLLECTED` quando houver dados MCP
+- `IC.<AGENT>_RAG_CONTEXT_RETRIEVED` quando RAG estiver habilitado
+
+O mixin `AgentRuntimeMixin` também emite:
+
+- `IC.MCP_TOOL_CALLED` antes da chamada MCP
+- `IC.TOOL_CALLED` após a chamada MCP
+
+## NOC
+
+O workflow já emite eventos operacionais principais:
+
+- `NOC.001` no início da execução
+- `NOC.005` em exceção fatal
+- `NOC.006` na persistência/finalização
+
+## GRL
+
+O backend agora também exemplifica emissão GRL no workflow:
+
+- `GRL.001` início do pipeline de guardrails
+- `GRL.002` decisão allow
+- `GRL.004` decisão block
+- `GRL.009` decisão final agregada
+
+Quando `OutputSupervisor` está habilitado, ele continua sendo o principal mecanismo corporativo de supervisão de saída.
+
+## Garantia
+
+A lógica original dos agentes não foi substituída por stubs. As chamadas LLM, MCP, RAG, cache e os retornos originais foram preservados.

Tuning-Performance/Transaction_Evidence/agent_template_backend/docs/LANGFUSE_SINGLE_TRACE_OBSERVER_FIX.md

@@ -0,0 +1,5 @@
+# Langfuse single trace observer fix
+
+This backend now uses `TelemetryBackedAgentObserver` instead of publishing IC/NOC/GRL through `AgentObserver(analytics=...)`.
+
+Why: when analytics includes the Langfuse provider, observer events such as `IC.AGENT_COMPLETED` and `NOC.006` may create a second root trace with little detail. Emitting those events through `Telemetry.event(...)` keeps them inside the active request/workflow trace.

Tuning-Performance/Transaction_Evidence/agent_template_backend/docs/TESTE_LONG_TERM_MEMORY.md

@@ -0,0 +1,82 @@
+# Teste e diagnóstico de Long-Term Memory
+
+## O que foi corrigido
+
+1. A LTM agora é carregada explicitamente antes do roteamento.
+2. O estado recebe uma chave estável em `long_term_memory_subject_key`, baseada em `business_context.customer_key` e, como fallback, `user_id`.
+3. O resultado de carga e persistência aparece em `metadata.long_term_memory` da resposta.
+4. `/health` informa a configuração efetiva de LTM carregada pelo processo.
+5. Falhas de leitura e gravação geram eventos `long_term_memory.load.failed` e `long_term_memory.persist.failed`.
+
+## Teste
+
+Primeira sessão:
+
+```bash
+curl -s http://localhost:8000/gateway/message \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "channel":"web",
+    "payload":{
+      "text":"Meu nome preferido é Cris e minha linguagem preferida é Python.",
+      "session_id":"ltm-session-001",
+      "user_id":"ltm-user-001",
+      "customer_id":"ltm-customer-001"
+    }
+  }'
+```
+
+Verifique na resposta:
+
+```json
+"long_term_memory": {
+  "subject_key": "ltm-customer-001",
+  "write_result": {
+    "saved": 2
+  }
+}
+```
+
+Nova sessão, mesma identidade:
+
+```bash
+curl -s http://localhost:8000/gateway/message \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "channel":"web",
+    "payload":{
+      "text":"Qual é meu nome preferido e qual linguagem eu prefiro?",
+      "session_id":"ltm-session-002",
+      "user_id":"ltm-user-001",
+      "customer_id":"ltm-customer-001"
+    }
+  }'
+```
+
+Na segunda resposta, confira:
+
+- `metadata.long_term_memory.subject_key` igual à primeira chamada;
+- `metadata.long_term_memory.loaded` com registros;
+- `metadata.long_term_memory.context` preenchido;
+- ausência de `load_error`.
+
+## Diagnóstico rápido
+
+```bash
+curl -s http://localhost:8000/health
+```
+
+A seção `long_term_memory` deve mostrar:
+
+```json
+{
+  "enabled": true,
+  "provider": "sqlite",
+  "sqlite_path": "./data/agent_framework.db",
+  "table": "agentfw_long_term_memory",
+  "auto_extract": true,
+  "inject_context": true
+}
+```
+
+Execute o backend com o diretório do projeto como diretório de trabalho. Como o caminho SQLite é relativo, iniciar a aplicação em outro diretório pode criar ou consultar outro arquivo `./data/agent_framework.db`.

Tuning-Performance/Transaction_Evidence/agent_template_backend/docs/VALIDACAO_BACKEND_IC_NOC_GRL.md

@@ -0,0 +1,62 @@
+# Validação da versão com IC/NOC/GRL
+
+Validações executadas nesta geração:
+
+1. `python -m compileall -q agent_template_backend/app`
+   - Resultado: OK.
+
+2. Smoke test dos agentes com LLM fake e Observer fake:
+   - `BillingAgent`: preservou resposta gerada pelo LLM e emitiu IC de início/fim.
+   - `OrdersAgent`: preservou resposta gerada pelo LLM e emitiu IC de início/fim.
+   - `ProductAgent`: preservou resposta gerada pelo LLM e emitiu IC de início/fim.
+   - `SupportAgent`: preservou resposta gerada pelo LLM e emitiu IC de início/fim.
+
+3. Verificação de regressão:
+   - Nenhum agente retorna `Template Enterprise ativo`.
+   - A lógica LLM/MCP/RAG/cache existente foi preservada.
+
+## Eventos adicionados
+
+### IC
+
+Nos agentes:
+
+- `IC.BILLING_AGENT_STARTED`
+- `IC.BILLING_MCP_CONTEXT_COLLECTED`
+- `IC.BILLING_RAG_CONTEXT_RETRIEVED`
+- `IC.BILLING_AGENT_COMPLETED`
+- `IC.ORDERS_AGENT_STARTED`
+- `IC.ORDERS_MCP_CONTEXT_COLLECTED`
+- `IC.ORDERS_RAG_CONTEXT_RETRIEVED`
+- `IC.ORDERS_AGENT_COMPLETED`
+- `IC.PRODUCT_AGENT_STARTED`
+- `IC.PRODUCT_MCP_CONTEXT_COLLECTED`
+- `IC.PRODUCT_RAG_CONTEXT_RETRIEVED`
+- `IC.PRODUCT_AGENT_COMPLETED`
+- `IC.SUPPORT_AGENT_STARTED`
+- `IC.SUPPORT_MCP_CONTEXT_COLLECTED`
+- `IC.SUPPORT_RAG_CONTEXT_RETRIEVED`
+- `IC.SUPPORT_AGENT_COMPLETED`
+
+No runtime MCP:
+
+- `IC.MCP_TOOL_CALLED`
+- `IC.TOOL_CALLED`
+
+### NOC
+
+Já integrados no workflow:
+
+- `NOC.001` início da execução
+- `NOC.005` erro fatal
+- `NOC.006` finalização/persistência
+
+### GRL
+
+No workflow de guardrails:
+
+- `GRL.001` início da avaliação
+- `GRL.002` allow
+- `GRL.004` block
+- `GRL.009` decisão final
+

Tuning-Performance/Transaction_Evidence/agent_template_backend/docs/VALIDACAO_TEMPLATE_ENTERPRISE.txt

@@ -0,0 +1,3 @@
+compileall app: OK
+Arquivos de exemplos IC/NOC/GRL adicionados.
+Agentes preservam implementação original comentada.

Tuning-Performance/Transaction_Evidence/agent_template_backend/llm_profiles.yaml

@@ -0,0 +1,80 @@
+profiles:
+  default:
+    provider: oci_openai
+    model: openai.gpt-4.1
+    temperature: 0.2
+    max_tokens: 2048
+  supervisor:
+    provider: oci_openai
+    model: openai.gpt-4.1
+    temperature: 0
+    max_tokens: 700
+  route_continuity:
+    provider: oci_openai
+    model: openai.gpt-4.1-mini
+    temperature: 0
+    max_tokens: 80
+    timeout_seconds: 5
+  router:
+    provider: oci_openai
+    model: openai.gpt-4.1
+    temperature: 0
+    max_tokens: 500
+  guardrail:
+    provider: oci_openai
+    model: openai.gpt-4.1
+    temperature: 0
+    max_tokens: 600
+  grl:
+    provider: oci_openai
+    model: openai.gpt-4.1
+    temperature: 0
+    max_tokens: 700
+  judge:
+    provider: oci_openai
+    model: openai.gpt-4.1
+    temperature: 0
+    max_tokens: 800
+  rag_rewriter:
+    provider: oci_openai
+    model: openai.gpt-4.1
+    temperature: 0
+    max_tokens: 300
+  rag_compressor:
+    provider: oci_openai
+    model: openai.gpt-4.1
+    temperature: 0
+    max_tokens: 1200
+  rag_generation:
+    provider: oci_openai
+    model: openai.gpt-4.1
+    temperature: 0.1
+    max_tokens: 1800
+  summary_memory:
+    provider: oci_openai
+    model: openai.gpt-4.1
+    temperature: 0.1
+    max_tokens: 1200
+  noc:
+    provider: oci_openai
+    model: openai.gpt-4.1
+    temperature: 0
+    max_tokens: 700
+  billing_agent:
+    provider: oci_openai
+    model: openai.gpt-4.1
+    temperature: 0.2
+  product_agent:
+    provider: oci_openai
+    model: openai.gpt-4.1
+    temperature: 0.2
+  backoffice_agent:
+    provider: oci_openai
+    model: openai.gpt-4.1
+    temperature: 0.2
+  mcp_parameter_extraction:
+    provider: oci_openai
+    model: openai.gpt-4.1-mini
+    temperature: 0
+    max_tokens: 80
+    timeout_seconds: 5

Tuning-Performance/Transaction_Evidence/agent_template_backend/requirements.txt

@@ -0,0 +1,23 @@
+fastapi>=0.115.0
+uvicorn[standard]>=0.30.0
+pydantic>=2.8.0
+pydantic-settings>=2.4.0
+python-dotenv>=1.0.1
+langgraph>=0.2.60
+langchain-core>=0.3.0
+openai>=1.60.0
+oci>=2.130.0
+oracledb>=2.4.0
+pymongo>=4.8.0
+redis>=5.0.0
+PyYAML>=6.0.2
+
+langfuse>=3.0.0
+httpx>=0.27.0
+opentelemetry-api>=1.27.0
+opentelemetry-sdk>=1.27.0
+opentelemetry-exporter-otlp-proto-http>=1.27.0
+
+pytest>=8.0.0
+pytest-asyncio>=0.23.0
+google-cloud-pubsub>=2.28.0

Tuning-Performance/Transaction_Evidence/agent_template_backend/scripts/test_long_term_memory.py

@@ -0,0 +1,29 @@
+import asyncio
+import tempfile
+from types import SimpleNamespace
+from agent_framework.memory.long_term_memory import create_long_term_memory_manager
+
+async def main():
+    with tempfile.TemporaryDirectory() as d:
+        settings = SimpleNamespace(
+            ENABLE_LONG_TERM_MEMORY=True,
+            LONG_TERM_MEMORY_PROVIDER='sqlite',
+            LONG_TERM_MEMORY_SQLITE_PATH=f'{d}/memory.db',
+            LONG_TERM_MEMORY_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,
+        )
+        manager = create_long_term_memory_manager(settings)
+        first = {'tenant_id':'default','agent_id':'memory_test','session_id':'a','user_text':'Me chame de Cris. Minha linguagem preferida é Python. Meu projeto atual se chama Atlas.','context':{'business_context':{'customer_key':'MEM-001'}}}
+        assert (await manager.persist_turn(first))['saved'] >= 3
+        second = {'tenant_id':'default','agent_id':'memory_test','session_id':'b','context':{'business_context':{'customer_key':'MEM-001'}}}
+        values = {item.key:item.value for item in await manager.load(second)}
+        assert values['preferred_name'].lower() == 'cris'
+        assert values['preferred_language'].lower() == 'python'
+        assert values['current_project'].lower() == 'atlas'
+        isolated = {'tenant_id':'default','agent_id':'memory_test','session_id':'c','context':{'business_context':{'customer_key':'MEM-002'}}}
+        assert await manager.load(isolated) == []
+        print('OK: persistência, recuperação entre sessões e isolamento validados')
+
+asyncio.run(main())

Tuning-Performance/Transaction_Evidence/agent_template_backend/workflows/devolucao_pedido.active.yaml

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

Tuning-Performance/Transaction_Evidence/agent_template_backend/workflows/devolucao_pedido.v1.yaml

@@ -0,0 +1,27 @@
+name: devolucao_pedido
+version: 1
+start: validar_pedido
+nodes:
+  - id: validar_pedido
+    action: validar_pedido
+    input:
+      order_id: $.input.order_id
+  - id: registrar_devolucao
+    action: registrar_devolucao
+    retry: 1
+    input:
+      order_id: $.input.order_id
+      reason: $.input.reason
+edges:
+  - from: validar_pedido
+    to: registrar_devolucao
+    when:
+      path: $.nodes.validar_pedido.valid
+      equals: true
+  - from: validar_pedido
+    to: END
+    when:
+      path: $.nodes.validar_pedido.valid
+      equals: false
+  - from: registrar_devolucao
+    to: END

docs/TRANSACTION_OPERATIONAL_EVIDENCE_FIX.md

@@ -0,0 +1,28 @@
+# Transaction Operational Evidence
+
+## Problem
+
+A confirmed transactional tool result was available only in the execution turn. On a later read-only turn, conversational memory could still mention the prior transaction (for example, a cancellation protocol), while the groundedness judge received only the current MCP results. This could classify a factually correct follow-up as unsupported.
+
+## Fix
+
+The framework now records completed/failed transactional tool outcomes as bounded operational evidence in LangGraph state/checkpoint (`transaction_evidence`). This is operational state, not Long Term Memory.
+
+For each new turn, the runtime correlates previous transaction evidence with the current resource using generic identifiers (`*_id`, `order_id`, `invoice_id`, `asset_id`, `resource_key`, etc.). Only relevant evidence is materialized as `relevant_transaction_evidence`.
+
+The same relevant evidence is:
+
+- injected into the answering LLM prompt;
+- merged with current MCP results for groundedness judges;
+- exposed in response metadata as `transaction_evidence` for diagnostics;
+- emitted with the completion telemetry event.
+
+The history is bounded to the 10 most recent transaction outcomes, and at most 5 correlated entries are injected for a turn.
+
+## Expected retail example
+
+1. `cancelar_pedido(PED-1001)` returns protocol `CANCEL-2026-001`.
+2. The result is persisted as transaction evidence.
+3. The next `consultar_pedido(PED-1001)` returns `EM_TRANSPORTE`.
+4. The answering agent and groundedness judge receive both the current order result and the prior cancellation evidence.
+5. A response that mentions `CANCEL-2026-001` is grounded rather than treated as an unsupported claim.

libs/agent_framework/src/agent_framework/runtime/agent_runtime.py

@@ -1448,14 +1448,122 @@ class AgentRuntimeMixin:
         state["active_transaction"] = tx
         return tx
 
-    def _finish_active_transaction(self, state: dict[str, Any], status: str) -> None:
+    @staticmethod
-        """Close the active transaction without leaving operational state behind."""
+    def _collect_resource_identifiers(value: Any) -> set[tuple[str, str]]:
+        """Collect stable business/resource identifiers from nested evidence.
+
+        Identifier names are deliberately generic (``*_id`` plus common business
+        keys) so the framework can correlate transaction evidence across domains
+        without embedding telecom/retail-specific behavior.
+        """
+        identifiers: set[tuple[str, str]] = set()
+        common = {
+            "resource_key", "customer_key", "contract_key", "account_key",
+            "session_key", "subject", "msisdn", "order_id", "invoice_id",
+            "asset_id", "product_id", "service_id", "protocol", "protocolo",
+        }
+
+        def walk(item: Any) -> None:
+            if isinstance(item, dict):
+                for key, raw in item.items():
+                    key_s = str(key).strip().lower()
+                    if raw not in (None, "", [], {}) and (key_s.endswith("_id") or key_s in common):
+                        if isinstance(raw, (str, int, float, bool)):
+                            identifiers.add((key_s, str(raw).strip().lower()))
+                    walk(raw)
+            elif isinstance(item, (list, tuple, set)):
+                for child in item:
+                    walk(child)
+
+        walk(value)
+        return identifiers
+
+    def _record_transaction_evidence(
+        self,
+        state: dict[str, Any],
+        *,
+        transaction: dict[str, Any] | None,
+        status: str,
+        result: dict[str, Any] | None,
+    ) -> None:
+        """Persist compact structured evidence from an executed transaction.
+
+        This is operational evidence, not semantic/LTM memory. It survives later
+        turns through the LangGraph state/checkpoint and can be used both by the
+        answering LLM and groundedness judges.
+        """
+        if not isinstance(transaction, dict) or not transaction.get("tool_name"):
+            return
+        # Only execution outcomes are evidence. A rejected/not-yet-executed action
+        # must not become a factual claim about the external system.
+        if status not in {"COMPLETED", "FAILED"} or not isinstance(result, dict):
+            return
+
+        evidence = {
+            "transaction_id": transaction.get("transaction_id"),
+            "tool_name": transaction.get("tool_name"),
+            "arguments": dict(transaction.get("arguments") or {}),
+            "status": status,
+            "started_from_intent": transaction.get("started_from_intent"),
+            "result": result,
+        }
+        history = [x for x in (state.get("transaction_evidence") or []) if isinstance(x, dict)]
+        txid = evidence.get("transaction_id")
+        if txid:
+            history = [x for x in history if x.get("transaction_id") != txid]
+        history.append(evidence)
+        # Bound checkpoint growth while retaining enough recent operational history.
+        state["transaction_evidence"] = history[-10:]
+        state["last_transaction_evidence"] = evidence
+
+    def transaction_evidence_for_turn(
+        self,
+        state: dict[str, Any],
+        mcp_results: list[dict[str, Any]] | None = None,
+    ) -> list[dict[str, Any]]:
+        """Return transaction evidence relevant to the current resource/turn."""
+        history = [x for x in (state.get("transaction_evidence") or []) if isinstance(x, dict)]
+        if not history:
+            return []
+
+        current_identifiers = self._collect_resource_identifiers(mcp_results or [])
+        if not current_identifiers:
+            current_identifiers |= self._collect_resource_identifiers(state.get("business_context") or {})
+
+        if current_identifiers:
+            relevant = []
+            for evidence in history:
+                evidence_ids = self._collect_resource_identifiers(evidence)
+                # Match by value as well as key: integrations sometimes rename
+                # resource identifiers between transaction/read models.
+                current_values = {value for _, value in current_identifiers}
+                evidence_values = {value for _, value in evidence_ids}
+                if current_identifiers & evidence_ids or current_values & evidence_values:
+                    relevant.append(evidence)
+            return relevant[-5:]
+
+        # With no resource identifier, expose only the latest evidence to avoid
+        # leaking unrelated historical operations into a new topic.
+        return history[-1:]
+
+    def _finish_active_transaction(
+        self,
+        state: dict[str, Any],
+        status: str,
+        *,
+        result: dict[str, Any] | None = None,
+    ) -> None:
+        """Close the active transaction and retain its result as operational evidence."""
         active = self._active_transaction(state)
         if isinstance(active, dict):
             state["last_transaction"] = {
                 **active,
                 "status": status,
+                **({"result": result} if isinstance(result, dict) else {}),
             }
+            self._record_transaction_evidence(
+                state, transaction=active, status=status, result=result
+            )
         state["active_transaction"] = None
         state["selected_tool_call"] = {}
         state["pending_tool_call"] = {}
@@ -1499,6 +1607,7 @@ class AgentRuntimeMixin:
             "transaction_status", "confirmation_required", "confirmation_received",
             "tool_policy_result", "missing_parameters", "next_state", "pending_domain_workflow", "pending_tool_clarification",
             "business_workflows_executed", "active_transaction", "last_transaction",
+            "transaction_evidence", "last_transaction_evidence", "relevant_transaction_evidence",
         )
         return {key: state.get(key) for key in keys if key in state}
 
@@ -1936,7 +2045,7 @@ class AgentRuntimeMixin:
                 self._capture_pending_tool_clarification(state, result, tool_name=tool_name, arguments=arguments)
                 final_status = ("WORKFLOW_PAUSED" if state.get("pending_domain_workflow") else ("TOOL_RESULT_CLARIFICATION" if state.get("pending_tool_clarification") else ("COMPLETED" if result.get("ok") else "FAILED")))
                 if final_status in _TERMINAL_TRANSACTION_STATUSES:
-                    self._finish_active_transaction(state, final_status)
+                    self._finish_active_transaction(state, final_status, result=result)
                 else:
                     state.update({
                         "transaction_status": final_status,
@@ -1969,7 +2078,7 @@ class AgentRuntimeMixin:
                 final_status = ("WORKFLOW_PAUSED" if state.get("pending_domain_workflow") else ("TOOL_RESULT_CLARIFICATION" if state.get("pending_tool_clarification") else ("COMPLETED" if result.get("ok") else "FAILED")))
                 state["tool_policy_result"] = {"action": "executed_after_confirmation", "tool_name": tool_name}
                 if final_status in _TERMINAL_TRANSACTION_STATUSES:
-                    self._finish_active_transaction(state, final_status)
+                    self._finish_active_transaction(state, final_status, result=result)
                 else:
                     state.update({
                         "transaction_status": final_status,
@@ -2102,7 +2211,7 @@ class AgentRuntimeMixin:
         self._capture_pending_domain_workflow(state, result)
         final_status = ("WORKFLOW_PAUSED" if state.get("pending_domain_workflow") else ("TOOL_RESULT_CLARIFICATION" if state.get("pending_tool_clarification") else ("COMPLETED" if result.get("ok") else "FAILED")))
         if final_status in _TERMINAL_TRANSACTION_STATUSES:
-            self._finish_active_transaction(state, final_status)
+            self._finish_active_transaction(state, final_status, result=result)
         else:
             state.update({
                 "transaction_status": final_status,
@@ -2117,7 +2226,12 @@ class AgentRuntimeMixin:
         return results
 
     async def _collect_mcp_context(self, state: dict[str, Any]) -> list[dict[str, Any]]:
-        return await self.execute_tools_for_intent(state)
+        results = await self.execute_tools_for_intent(state)
+        # Materialize the relevant prior operational evidence in graph state so
+        # downstream nodes (output supervision/judges/telemetry) consume the same
+        # evidence set used by the answering agent.
+        state["relevant_transaction_evidence"] = self.transaction_evidence_for_turn(state, results)
+        return results
 
     # ------------------------------------------------------------------
     # Conversation memory / context compression
@@ -2271,6 +2385,12 @@ class AgentRuntimeMixin:
             sections.append(f"BusinessContext canônico:\n{runtime.business_context or '[sem business_context]'}")
         if mcp_results is not None:
             sections.append(f"Resultados MCP normalizados pelo framework:\n{mcp_results}")
+        transaction_evidence = self.transaction_evidence_for_turn(state, mcp_results)
+        if transaction_evidence:
+            sections.append(
+                "Evidências operacionais de transações anteriores relevantes ao recurso atual "
+                f"(persistidas pelo framework, não inferidas pela memória conversacional):\n{transaction_evidence}"
+            )
         if rag_context is not None:
             sections.append(f"Contexto RAG nativo do framework:\n{rag_context or '[sem contexto RAG]'}")
         if rag_metadata is not None:

templates/agent_template_backend/app/main.py

@@ -460,6 +460,7 @@ async def _process_gateway_message(req: GatewayRequest, emit_sse: bool = False)
                     "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"),

templates/agent_template_backend/app/state.py

@@ -27,6 +27,9 @@ class AgentState(TypedDict, total=False):
     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]