first commit

templates/agent_template_backend/.env

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+###############################################################################
+# AI AGENT PLATFORM - CONFIGURAÇÃO ÚNICA
+# Este arquivo é lido por Pydantic Settings no framework e no backend template.
+###############################################################################
+
+APP_NAME=ai-agent-template
+APP_ENV=local
+LOG_LEVEL=INFO
+API_HOST=0.0.0.0
+API_PORT=8000
+CORS_ORIGINS=http://localhost:5173,http://127.0.0.1:5173
+
+###############################################################################
+# LLM - OCI Generative AI como provider principal
+###############################################################################
+# Opções: mock, oci_openai, oci_sdk, openai_compatible
+LLM_PROVIDER=oci_openai
+LLM_TEMPERATURE=0.2
+LLM_MAX_TOKENS=2048
+LLM_TIMEOUT_SECONDS=120
+
+# OCI OpenAI-compatible endpoint
+OCI_GENAI_BASE_URL=https://inference.generativeai.us-chicago-1.oci.oraclecloud.com/openai/v1
+OCI_GENAI_MODEL=openai.gpt-4.1
+OCI_GENAI_API_KEY=sk-ph3FgX6ph3FgX6ph3FgX6ph3FgX6ph3FgX6ph3FgX6
+OCI_GENAI_PROJECT_OCID=
+
+# OCI_AUTH_MODE=config_file|instance_principal|resource_principal
+OCI_AUTH_MODE=config_file
+# OCI SDK / signer / profiles
+OCI_CONFIG_FILE=~/.oci/config
+OCI_PROFILE=DEFAULT
+OCI_COMPARTMENT_ID=ocid1.compartment.oc1..aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa
+OCI_REGION=us-chicago-1
+
+###############################################################################
+# Persistência
+###############################################################################
+# Opções: memory, autonomous, mongodb
+SESSION_REPOSITORY_PROVIDER=autonomous
+MEMORY_REPOSITORY_PROVIDER=autonomous
+CHECKPOINT_REPOSITORY_PROVIDER=autonomous
+
+# Autonomous Database
+ADB_USER=admin
+ADB_PASSWORD=fjhsdf04954hf
+ADB_DSN=oradb23aidev_high
+ADB_WALLET_LOCATION=/ORACLE/DEFAULT/Wallet_ORADB23aiDev
+ADB_WALLET_PASSWORD=fjhsdf04954hf
+ADB_TABLE_PREFIX=AGENTFW
+
+# MongoDB - também pode representar Autonomous usando API compatível com Mongo, se habilitada no ambiente
+MONGODB_URI=mongodb://mongo:mongopassword@localhost:27017
+MONGODB_DATABASE=agent_platform
+
+# Redis
+REDIS_URL=redis://localhost:6379/0
+ENABLE_REDIS_CACHE=false
+
+###############################################################################
+# RAG / Vector / Graph
+###############################################################################
+VECTOR_STORE_PROVIDER=memory
+GRAPH_STORE_PROVIDER=memory
+RAG_TOP_K=5
+EMBEDDING_PROVIDER=mock
+OCI_EMBEDDING_MODEL=cohere.embed-multilingual-v3.0
+RAG_FILE_GLOBS=*.md,*.txt,*.yaml,*.yml,*.json
+
+###############################################################################
+# Observabilidade
+###############################################################################
+ENABLE_LANGFUSE=true
+LANGFUSE_PUBLIC_KEY=pk-lf-bd9b0c7e-2b8b-4e5b-a382-284a9b4413b3
+LANGFUSE_SECRET_KEY=sk-lf-5f5cc18d-0bb5-424e-b5d0-cb3664d58c20
+LANGFUSE_HOST=http://localhost:3005
+ENABLE_OTEL=false
+OTEL_EXPORTER_OTLP_ENDPOINT=
+OTEL_SERVICE_NAME=ai-agent-template
+
+###############################################################################
+# 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
+
+###############################################################################
+# 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

templates/agent_template_backend/Dockerfile

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+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"]

templates/agent_template_backend/README_ENTERPRISE_TEMPLATE.md

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+# 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.

templates/agent_template_backend/app/agents/README.md

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+# 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.

templates/agent_template_backend/app/agents/billing_agent.py

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+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",
+            )
+
+        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"),
+        }
+
+        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)

templates/agent_template_backend/app/agents/orders_agent.py

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+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",
+            )
+
+        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"),
+        }
+
+        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)

templates/agent_template_backend/app/agents/product_agent.py

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+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",
+            )
+
+        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"),
+        }
+
+        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)

templates/agent_template_backend/app/agents/prompting.py

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+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}"

templates/agent_template_backend/app/agents/runtime.py

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+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
+
+__all__ = ["AgentRuntimeMixin", "MessageBuilder", "RuntimeContext"]

templates/agent_template_backend/app/agents/support_agent.py

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+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",
+            )
+
+        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"),
+        }
+
+        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)

templates/agent_template_backend/app/examples/__init__.py

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

templates/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",
+    )

templates/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",
+    )

templates/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

templates/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",
+    )

templates/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",
+    )

templates/agent_template_backend/app/main.py

@@ -0,0 +1,476 @@
+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.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):
+    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
+
+
+class GatewayRequest(BaseModel):
+    channel: str = "web"
+    payload: dict
+    agent_id: str | None = None
+    tenant_id: str | None = None
+
+
+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
+    identity, normalized_context, business_context, missing_identity_keys = _resolve_identity(req, msg)
+    agent_session_id = identity.conversation_key()
+    message_id = (req.payload or {}).get("message_id") or str(uuid4())
+    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,
+        ura_call_id=(req.payload or {}).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 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=msg.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)]
+
+        trace_input = {
+            "text": msg.text,
+            "channel": msg.channel,
+            "channel_id": msg.channel_id,
+            "tenant_id": identity.tenant_id,
+            "agent_id": identity.agent_id,
+            "conversation_key": agent_session_id,
+            "message_id": message_id,
+            "business_context": business_context.model_dump(),
+            "identity_missing": missing_identity_keys,
+        }
+
+        async with telemetry.span(
+            "agent.gateway_message",
+            session_id=agent_session_id,
+            user_id=session.user_id,
+            channel=msg.channel,
+            input=trace_input,
+            tags=["agent-template", msg.channel, f"agent:{identity.agent_id}", f"tenant:{identity.tenant_id}"],
+        ):
+            await telemetry.event("gateway.message.received", trace_input)
+            await sse_hub.emit(agent_session_id, "workflow.started", trace_input) 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,
+                    "agent_profile": normalized_context["agent_profile"],
+                    "user_text": msg.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,
+                        "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"),
+                },
+            ),
+        )
+
+        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,
+                "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"),
+                "business_context": business_context.model_dump(),
+                "identity_missing": missing_identity_keys,
+                "judges": result.get("judge_results"),
+                "guardrails": result.get("guardrail_decisions"),
+            },
+        )
+        rendered = await gateway.render(response)
+        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,
+        "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": msg.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()

templates/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})

templates/agent_template_backend/app/state.py

@@ -0,0 +1,34 @@
+from typing import Any, TypedDict
+
+
+class AgentState(TypedDict, total=False):
+    tenant_id: str
+    agent_id: str
+    session_id: str
+    conversation_key: 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]]
+    supervisor_plan: dict[str, Any]
+    supervisor_results: list[dict[str, Any]]
+    active_agent: str
+    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

templates/agent_template_backend/app/workflows/agent_graph.py

@@ -0,0 +1,705 @@
+from agent_framework.checkpoints.langgraph_saver import create_langgraph_checkpointer
+from langgraph.graph import END, START, StateGraph
+
+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
+
+
+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.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)
+        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 = StateGraph(AgentState)
+        builder.add_node("input_guardrails", self._node("input_guardrails", self.input_guardrails))
+        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("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", self._node("persist", self.persist))
+
+        builder.add_edge(START, "input_guardrails")
+        builder.add_conditional_edges(
+            "input_guardrails",
+            self._after_input_guardrails,
+            {"blocked": "persist", "continue": "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",
+                "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("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")
+        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):
+        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,
+            }
+
+    async def billing_agent(self, state):
+        async with self.langgraph_telemetry.node("billing_agent", 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.langgraph_telemetry.node("product_agent", 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.langgraph_telemetry.node("orders_agent", 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.langgraph_telemetry.node("support_agent", 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 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")},
+        ):
+            results = await self.judges.evaluate_all(
+                state["user_text"], state["final_answer"], state.get("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 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", []),
+                },
+            )
+
+            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

templates/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.

templates/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

templates/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

templates/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.

templates/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

templates/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

templates/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.

templates/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

templates/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

templates/agent_template_backend/config/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

templates/agent_template_backend/config/mcp_parameter_mapping.yaml

@@ -0,0 +1,58 @@
+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
+        contract_key: order_id
+        session_key: session_id
+    consultar_entrega:
+      map:
+        contract_key: order_id
+        session_key: session_id
+    solicitar_troca:
+      map:
+        contract_key: order_id
+        session_key: session_id
+      defaults:
+        reason: Solicitação aberta pelo atendimento conversacional.
+    solicitar_devolucao:
+      map:
+        contract_key: order_id
+        session_key: session_id
+      defaults:
+        reason: Solicitação aberta pelo atendimento conversacional.

templates/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.

templates/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

templates/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

templates/agent_template_backend/config/routing.yaml

@@ -0,0 +1,114 @@
+# 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.
+
+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
+      - produto
+      - 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_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: 40
+    mcp_tools:
+      - consultar_pedido
+      - solicitar_troca
+      - solicitar_devolucao
+    keywords:
+      - troca
+      - devolução
+      - devolver
+      - garantia
+      - defeito
+      - produto quebrado
+      - suporte
+      - arrependimento
+    examples:
+      - Quero trocar um produto.
+      - Meu produto veio com defeito.
+      - Como faço uma devolução?

templates/agent_template_backend/config/tools.yaml

@@ -0,0 +1,68 @@
+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
+
+  consultar_pagamentos:
+    description: Consulta histórico de pagamentos do cliente.
+    mcp_server: telecom
+    enabled: true
+    args_schema:
+      msisdn: string
+
+  consultar_plano:
+    description: Consulta plano ativo e atributos comerciais.
+    mcp_server: telecom
+    enabled: true
+    args_schema:
+      msisdn: string
+      asset_id: string
+
+  listar_servicos:
+    description: Lista serviços ativos e adicionais VAS.
+    mcp_server: telecom
+    enabled: true
+    args_schema:
+      msisdn: string
+
+  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
+
+  consultar_entrega:
+    description: Consulta entrega e rastreamento do pedido.
+    mcp_server: retail
+    enabled: true
+    args_schema:
+      order_id: string
+
+  solicitar_troca:
+    description: Simula abertura de solicitação de troca.
+    mcp_server: retail
+    enabled: true
+    tool_type: action
+    requires: [order_id, reason]
+    confirmation_required: false
+    args_schema:
+      order_id: string
+      reason: string
+
+  solicitar_devolucao:
+    description: Simula abertura de solicitação de devolução.
+    mcp_server: retail
+    enabled: true
+    tool_type: action
+    requires: [order_id, reason]
+    confirmation_required: false
+    args_schema:
+      order_id: string
+      reason: string
+

templates/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.

templates/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.

templates/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`.

templates/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
+```

templates/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`.

templates/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.

templates/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.

templates/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
+

templates/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.

templates/agent_template_backend/llm_profiles.yaml

@@ -0,0 +1,88 @@
+# Optional file. If this file is absent, the backend keeps using .env exactly as before.
+# If present, each inference point can override provider/model/params.
+profiles:
+  default:
+    provider: oci_openai
+    model: openai.gpt-4.1
+    temperature: 0.2
+    max_tokens: 2048
+
+  # Workflow/routing
+  supervisor:
+    provider: oci_openai
+    model: openai.gpt-4.1
+    temperature: 0
+    max_tokens: 700
+
+  router:
+    provider: oci_openai
+    model: openai.gpt-4.1
+    temperature: 0
+    max_tokens: 500
+
+  # Safety / evaluation
+  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
+  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: xopenai.gpt-4.1
+    temperature: 0.1
+    max_tokens: 1800
+
+  # Memory / operations
+  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
+
+  # Agent-specific overrides
+  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

templates/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