Melhorias no guardrails de FRASEOLOGY. Melhorias no Transaction Workflow

.env

@@ -1,209 +0,0 @@
-###############################################################################
-# AI AGENT PLATFORM - CONFIGURAÇÃO ÚNICA
-# Este arquivo é lido por Pydantic Settings no framework e no backend template.
-###############################################################################
-
-APP_NAME=ai-agent-template
-APP_ENV=local
-LOG_LEVEL=INFO
-API_HOST=0.0.0.0
-API_PORT=8000
-CORS_ORIGINS=http://localhost:5173,http://127.0.0.1:5173
-
-###############################################################################
-# LLM - OCI Generative AI como provider principal
-###############################################################################
-# Opções: mock, oci_openai, oci_sdk, openai_compatible
-LLM_PROVIDER=oci_openai
-LLM_TEMPERATURE=0.2
-LLM_MAX_TOKENS=2048
-LLM_TIMEOUT_SECONDS=120
-
-# OCI OpenAI-compatible endpoint
-OCI_GENAI_BASE_URL=https://inference.generativeai.us-chicago-1.oci.oraclecloud.com/openai/v1
-OCI_GENAI_MODEL=openai.gpt-4.1
-OCI_GENAI_API_KEY=sk-ph3FgX6ph3FgX6ph3FgX6ph3FgX6ph3FgX6ph3FgX6
-OCI_GENAI_PROJECT_OCID=
-
-# OCI_AUTH_MODE=config_file|instance_principal|resource_principal
-OCI_AUTH_MODE=config_file
-# OCI SDK / signer / profiles
-OCI_CONFIG_FILE=~/.oci/config
-OCI_PROFILE=DEFAULT
-OCI_COMPARTMENT_ID=ocid1.compartment.oc1..aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa
-OCI_REGION=us-chicago-1
-
-###############################################################################
-# Persistência
-###############################################################################
-# Opções: memory, autonomous, mongodb
-SESSION_REPOSITORY_PROVIDER=autonomous
-MEMORY_REPOSITORY_PROVIDER=autonomous
-CHECKPOINT_REPOSITORY_PROVIDER=autonomous
-
-# Autonomous Database
-ADB_USER=admin
-ADB_PASSWORD=fjhsdf04954hf
-ADB_DSN=oradb23aidev_high
-ADB_WALLET_LOCATION=/ORACLE/DEFAULT/Wallet_ORADB23aiDev
-ADB_WALLET_PASSWORD=fjhsdf04954hf
-ADB_TABLE_PREFIX=AGENTFW
-
-# MongoDB - também pode representar Autonomous usando API compatível com Mongo, se habilitada no ambiente
-MONGODB_URI=mongodb://mongo:mongopassword@localhost:27017
-MONGODB_DATABASE=agent_platform
-
-# Redis
-REDIS_URL=redis://localhost:6379/0
-ENABLE_REDIS_CACHE=false
-
-###############################################################################
-# RAG / Vector / Graph
-###############################################################################
-VECTOR_STORE_PROVIDER=memory
-GRAPH_STORE_PROVIDER=memory
-RAG_TOP_K=5
-EMBEDDING_PROVIDER=mock
-OCI_EMBEDDING_MODEL=cohere.embed-multilingual-v3.0
-RAG_FILE_GLOBS=*.md,*.txt,*.yaml,*.yml,*.json
-
-###############################################################################
-# Observabilidade
-###############################################################################
-ENABLE_LANGFUSE=true
-LANGFUSE_TRACE_MODE=verbose # Opcional: verbose, compact
-LANGFUSE_ROOT_SPAN_NAME=agent.gateway_message
-LANGFUSE_LEGACY_IO_FALLBACK=true
-LANGFUSE_PUBLIC_KEY=pk-lf-bd9b0c7e-2b8b-4e5b-a382-284a9b4413b3
-LANGFUSE_SECRET_KEY=sk-lf-5f5cc18d-0bb5-424e-b5d0-cb3664d58c20
-LANGFUSE_HOST=http://localhost:3005
-ENABLE_OTEL=false
-OTEL_EXPORTER_OTLP_ENDPOINT=
-OTEL_SERVICE_NAME=ai-agent-template
-ENABLE_LANGFUSE_OPENAI_AUTO_INSTRUMENTATION=true
-
-###############################################################################
-# Analytics / Observer corporativo
-###############################################################################
-# Quando true, AgentObserver publica eventos IC.*, NOC.* e GRL.* nos providers abaixo.
-ENABLE_ANALYTICS=false
-# Providers aceitos: oci_streaming,pubsub,noop
-ANALYTICS_PROVIDERS=pubsub
-# Compatibilidade FIRST/TIM: pode informar AGENT_PUBSUB_TOPIC diretamente.
-AGENT_PUBSUB_TOPIC=
-GCP_PUBSUB_TOPIC_PATH=
-GCP_PROJECT_ID=
-GCP_PUBSUB_TOPIC=
-GCP_PUBSUB_TIMEOUT_SECONDS=30
-# Credencial GCP segue padrão Google:
-# GOOGLE_APPLICATION_CREDENTIALS=/secrets/gcp-service-account.json
-
-###############################################################################
-# OCI Streaming
-###############################################################################
-ENABLE_OCI_STREAMING=false
-OCI_STREAM_ENDPOINT=
-OCI_STREAM_OCID=
-OCI_STREAM_PARTITION_KEY=agent-events
-
-###############################################################################
-# Guardrails, Judges, Supervisor
-###############################################################################
-ENABLE_INPUT_GUARDRAILS=true
-ENABLE_OUTPUT_GUARDRAILS=true
-ENABLE_JUDGES=true
-ENABLE_SUPERVISOR=true
-ENABLE_OUTPUT_SUPERVISOR=true
-ENABLE_PARALLEL_GUARDRAILS=true
-GUARDRAILS_FAIL_FAST=true
-OUTPUT_SUPERVISOR_MAX_RETRIES=3
-GUARDRAILS_CONFIG_PATH=./config/guardrails.yaml
-JUDGES_CONFIG_PATH=./config/judges.yaml
-PROMPT_POLICY_PATH=./config/prompt_policy.yaml
-
-###############################################################################
-# Gateway de canais
-###############################################################################
-DEFAULT_CHANNEL=web
-# embedded = backend may parse simple/native channel payloads.
-# external = backend only accepts GatewayRequest normalized by an external Channel Gateway.
-FRAMEWORK_CHANNEL_INPUT_MODE=embedded
-ENABLE_VOICE_ADAPTER=true
-ENABLE_WHATSAPP_ADAPTER=true
-ENABLE_TEXT_ADAPTER=true
-
-#################################################
-# ENTERPRISE ROUTING
-#################################################
-# Arquivo YAML com intents, keywords, políticas de estado e fallback.
-ROUTING_CONFIG_PATH=./config/routing.yaml
-# true = usa LLM para classificar quando keywords/estado não resolverem.
-# Em produção, costuma ser útil; em desenvolvimento, false evita custo e latência.
-ENABLE_LLM_ROUTER=true
-
-# Semantic route stickiness (optional).
-# Uses a lightweight LLM profile to decide only CONTINUE vs ROUTE.
-# There are no regexes or deterministic language rules.
-ENABLE_ROUTE_STICKINESS=false
-ROUTE_STICKINESS_LLM_PROFILE=route_continuity
-ROUTE_STICKINESS_CONFIDENCE_THRESHOLD=0.90
-ROUTE_STICKINESS_HISTORY_TURNS=2
-ROUTE_STICKINESS_MAX_TOKENS=80
-HUMAN_HANDOFF_MESSAGE=Vou encaminhar seu atendimento para uma pessoa.
-END_SESSION_MESSAGE=Atendimento encerrado. Obrigado pelo contato.
-
-###############################################################################
-# MCP / Tools
-###############################################################################
-ENABLE_MCP_TOOLS=true
-MCP_SERVERS_CONFIG_PATH=./config/mcp_servers.yaml
-TOOLS_CONFIG_PATH=./config/tools.yaml
-TOOL_POLICIES_PATH=./config/tool_policies.yaml
-MCP_TOOL_TIMEOUT_SECONDS=30
-
-# router = EnterpriseRouter seleciona um agente; supervisor = pode acionar múltiplos agentes
-ROUTING_MODE=router
-
-# Usage/cost accounting
-USAGE_REPOSITORY_PROVIDER=autonomous
-IDENTITY_CONFIG_PATH=./config/identity.yaml
-MCP_PARAMETER_MAPPING_PATH=./config/mcp_parameter_mapping.yaml
-
-# -----------------------------------------------------------------------------
-# ConversationSummaryMemory / compressão de contexto conversacional
-# -----------------------------------------------------------------------------
-ENABLE_CONVERSATION_SUMMARY_MEMORY=true
-MEMORY_CONTEXT_STRATEGY=summary
-MEMORY_HISTORY_LIMIT=80
-MEMORY_RECENT_MESSAGES_LIMIT=8
-MEMORY_SUMMARY_TRIGGER_MESSAGES=20
-MEMORY_MAX_SUMMARY_CHARS=6000
-MEMORY_SUMMARY_USE_LLM=true
-MEMORY_INJECT_RECENT_MESSAGES=true
-MEMORY_INJECT_SUMMARY=true
-
-###############################################################################
-# MCP Gateway
-###############################################################################
-# true = framework routes tool calls to the dedicated MCP Gateway.
-# false = framework calls MCP servers directly from mcp_servers.yaml.
-MCP_GATEWAY_ENABLED=true
-MCP_GATEWAY_URL=http://localhost:8300
-MCP_GATEWAY_TIMEOUT_SECONDS=60
-# MCP_GATEWAY_TOKEN=
-MCP_GATEWAY_AGENT_ID=telecom_contas
-MCP_GATEWAY_TENANT_ID=default
-
-###############################################################################
-# LONG-TERM MEMORY
-###############################################################################
-ENABLE_LONG_TERM_MEMORY=true
-LONG_TERM_MEMORY_PROVIDER=sqlite
-LONG_TERM_MEMORY_SQLITE_PATH=./data/agent_framework.db
-LONG_TERM_MEMORY_TABLE=agentfw_long_term_memory
-# For Autonomous/Oracle, defaults to ${ADB_TABLE_PREFIX}_LONG_TERM_MEMORY
-# LONG_TERM_MEMORY_ORACLE_TABLE=AGENTFW_LONG_TERM_MEMORY
-LONG_TERM_MEMORY_MAX_CONTEXT_ITEMS=20
-LONG_TERM_MEMORY_MIN_CONFIDENCE=0.70
-LONG_TERM_MEMORY_AUTO_EXTRACT=true
-LONG_TERM_MEMORY_INJECT_CONTEXT=true

libs/agent_framework/src/agent_framework/guardrails/rails.py

@@ -328,6 +328,47 @@ class ProactiveOfferRail(Guardrail):
         )
 
 
+def _sanitize_low_risk_phraseology(text: str, reason: str) -> str | None:
+    """Remove apenas fechamentos/redirecionamentos de baixo risco.
+
+    FRASEOLOGIA continua fail-closed para conteúdo material. Para B4 e ofertas
+    genéricas de continuação, porém, bloquear toda uma resposta grounded piora a
+    UX; nesses casos removemos somente a sentença ofensora.
+    """
+    normalized_reason = (reason or "").casefold()
+    low_risk = any(token in normalized_reason for token in (
+        "viola b4", "outro canal", "atendimento especializado",
+        "realizar alguma ação", "oferta de ação", "orienta o cliente",
+    ))
+    if not low_risk:
+        return None
+
+    forbidden = (
+        "entre em contato", "fale com um atendente", "procure uma loja",
+        "acesse o app", "acesse o site", "atendimento especializado",
+        "área de planos", "area de planos", "é só me avisar",
+        "e so me avisar", "realizar alguma ação", "realizar alguma acao",
+        "gerenciar esses serviços", "gerenciar esses servicos",
+    )
+    sentences = re.split(r"(?<=[.!?])\s+", (text or "").strip())
+    kept: list[str] = []
+    removed = False
+    for sentence in sentences:
+        normalized = sentence.casefold()
+        proactive = (
+            ("se quiser" in normalized or "caso queira" in normalized or "se desejar" in normalized)
+            and any(token in normalized for token in ("realizar", "gerenciar", "cancelar", "contratar", "alterar", "ação", "acao"))
+        )
+        if proactive or any(token in normalized for token in forbidden):
+            removed = True
+            continue
+        kept.append(sentence.strip())
+    sanitized = " ".join(x for x in kept if x).strip()
+    if removed and sanitized:
+        return sanitized
+    return None
+
+
 class PhraseologyRail(Guardrail):
     """FRASEOLOGIA calibrado: bloqueia fraseados proibidos do agente."""
     code = "FRASEOLOGIA"
@@ -339,9 +380,26 @@ class PhraseologyRail(Guardrail):
             _llm(ctx), "FRASEOLOGIA", {"text": text or "", "context": ctx},
             profile_name="grl", component_name="guardrail.fraseologia", generation_name="guardrail.fraseologia",
         )
+        allowed = bool(out.get("allowed", True))
+        reason = str(out.get("reason") or out.get("label") or "FRASEOLOGIA avaliado")
+        if not allowed:
+            sanitized = _sanitize_low_risk_phraseology(text or "", reason)
+            if sanitized:
                 return RailDecision(
-            code=self.code, allowed=bool(out.get("allowed", True)),
+                    code=self.code,
-            reason=str(out.get("reason") or out.get("label") or "FRASEOLOGIA avaliado"),
+                    allowed=True,
+                    reason=f"FRASEOLOGIA sanitizada: {reason}",
+                    sanitized_text=sanitized,
+                    metadata={
+                        "mechanism": "llm_rail+deterministic_sanitize",
+                        "data": out,
+                        "calibrated": True,
+                        "original_allowed": False,
+                    },
+                )
+        return RailDecision(
+            code=self.code, allowed=allowed,
+            reason=reason,
             sanitized_text=text, metadata={"mechanism": "llm_rail", "data": out, "calibrated": True},
         )
 

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

@@ -1167,7 +1167,25 @@ class AgentRuntimeMixin:
         return None
 
     def _select_transactional_tool(self, tools: list[str], text: str) -> str | None:
-        return self._transactional_action_match(text, tools)
+        """Seleciona a ação transacional da intent atual.
+
+        O match por ``selection_keywords`` continua tendo precedência. Porém, depois
+        que o EnterpriseRouter já restringiu ``tools`` às capabilities da intent,
+        uma única tool transacional é uma escolha determinística e segura. Isso
+        evita perder frases naturais como ``quero cancelar TIM Fashion Mensal`` ou
+        ``não contratei esse serviço`` só porque elas não repetem literalmente uma
+        keyword de ``tools.yaml``.
+        """
+        matched = self._transactional_action_match(text, tools)
+        if matched:
+            return matched
+
+        transactional = [
+            tool
+            for tool in tools
+            if self._resolve_tool_execution_policy(tool).get("operation_type") == "transactional"
+        ]
+        return transactional[0] if len(transactional) == 1 else None
 
     @staticmethod
     def _agent_state_prefix(agent_name: str | None) -> str:
@@ -1521,6 +1539,11 @@ class AgentRuntimeMixin:
                     **previous_args,
                     **{k: v for k, v in new_args.items() if v not in (None, "", [], {})},
                 }
+                # Execute parameter extraction before deciding whether the workflow
+                # must enter COLLECTING_PARAMETERS. Otherwise parameters declared
+                # with strategy=llm in mcp_parameter_mapping.yaml are invisible to
+                # the deterministic transaction state machine.
+                arguments = await self._extract_mcp_parameters(tool_name, arguments, state)
                 policy = self._resolve_tool_execution_policy(tool_name, arguments)
                 missing = self._missing_required_arguments(policy, arguments)
                 if missing:
@@ -1653,6 +1676,9 @@ class AgentRuntimeMixin:
             aliases=aliases,
             extra_args=self._extract_action_arguments(text),
         )
+        # Extract parameters (including LLM-declared extraction rules) before
+        # validating required fields and before persisting the pending call.
+        action_args = await self._extract_mcp_parameters(selected_action, action_args, state)
         policy = self._resolve_tool_execution_policy(selected_action, action_args)
         selected = {"tool_name": selected_action, "arguments": action_args}
         state["selected_tool_call"] = selected