nova funcionalidade: reconciliacao temporal

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

@@ -323,10 +323,28 @@ class CoherenceRail(Guardrail):
                     "data": out,
                 },
             )
+        try:
             out = await classify_with_framework_llm(
                 _llm(ctx), "COER", {"text": text or "", "context": ctx},
                 profile_name="guardrail", component_name="guardrail.coer", generation_name="guardrail.coer",
             )
+        except Exception as exc:
+            # COER is a conversational-coherence rail, not a security boundary.
+            # If its semantic classifier/provider is unavailable, the graph must
+            # continue and let the normal router/workflow clarification logic own
+            # the turn instead of surfacing an infrastructure exception.
+            return RailDecision(
+                code=self.code,
+                allowed=True,
+                reason="Classificador de coerência indisponível; continuidade delegada ao runtime",
+                sanitized_text=text,
+                metadata={
+                    "mechanism": "infrastructure_fail_open",
+                    "calibrated": True,
+                    "error_type": type(exc).__name__,
+                    "error": str(exc)[:500],
+                },
+            )
         return RailDecision(
             code=self.code, allowed=bool(out.get("allowed", True)),
             reason=str(out.get("reason") or out.get("label") or "COER avaliado"),

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

@@ -12,7 +12,7 @@ from typing import Any, Iterable, Mapping
 
 
 from agent_framework.memory.summary_memory import MemoryContext, render_recent_messages
-from agent_framework.runtime.transaction_parameters import extract_transaction_parameters, parse_transaction_confirmation
+from agent_framework.runtime.transaction_parameters import extract_transaction_parameters, reconcile_transaction_parameters, parse_transaction_confirmation
 from agent_framework.workflows.input_contract import match_expected_input
 
 
@@ -1466,6 +1466,153 @@ class AgentRuntimeMixin:
         cfg = self._tool_config(tool_name)
         return str(getattr(cfg, "description", "") or "") if cfg is not None else ""
 
+    @staticmethod
+    def _transaction_context_newest_first(context: Any) -> str:
+        """Normalize bounded conversation text into newest->oldest message blocks.
+
+        Role-labelled multi-line messages are kept intact.  This avoids reversing
+        individual lines inside an assistant explanation while still honoring the
+        temporal search order required by parameter reconciliation.
+        """
+        raw = str(context or "").strip()
+        if not raw:
+            return ""
+        blocks: list[str] = []
+        current: list[str] = []
+        for line in raw.splitlines():
+            stripped = line.strip()
+            if not stripped:
+                continue
+            low = stripped.lower()
+            starts_role = low.startswith(("user:", "assistant:", "system:", "cliente:", "agente:"))
+            if starts_role and current:
+                blocks.append(" ".join(current))
+                current = [stripped]
+            else:
+                current.append(stripped)
+        if current:
+            blocks.append(" ".join(current))
+        if not blocks:
+            blocks = [line.strip() for line in raw.splitlines() if line.strip()]
+        newest = list(reversed(blocks))
+        return "\n".join(f"history:{i+1}: {block}" for i, block in enumerate(newest))
+
+    async def _reconcile_transaction_parameters(
+        self,
+        state: dict[str, Any],
+        *,
+        tool_name: str,
+        parameter_names: list[str],
+        known_arguments: dict[str, Any] | None = None,
+    ) -> dict[str, Any]:
+        """Reconcile a coherent required-parameter set from text and tool schema."""
+        # Compatibility/extensibility: if a domain/runtime subclass explicitly
+        # overrides the legacy extractor, honor that override and translate its
+        # candidates into reconciliation decisions. The base framework path below
+        # remains the coherent temporal reconciler.
+        override = getattr(type(self), "_extract_transaction_parameters", None)
+        if override is not None and override is not AgentRuntimeMixin._extract_transaction_parameters:
+            route_meta_compat = ((state.get("route_decision") or {}).get("metadata") or {}) if isinstance(state.get("route_decision"), dict) else {}
+            compat_fields = list(parameter_names) if route_meta_compat.get("transaction_parameter_values") else [
+                name for name in parameter_names if (known_arguments or {}).get(name) in _EMPTY_VALUES
+            ]
+            extracted = await override(
+                self, state, tool_name=tool_name, missing_parameters=compat_fields,
+                known_arguments=dict(known_arguments or {}),
+            )
+            values = dict(extracted or {})
+            decisions = {name: ("resolved" if name in values else ("preserve" if (known_arguments or {}).get(name) not in _EMPTY_VALUES else "unresolved")) for name in parameter_names}
+            provenance = {name: ("current" if name in values else "state") for name in parameter_names if decisions[name] != "unresolved"}
+            return {"values": values, "decisions": decisions, "provenance": provenance, "clear_fields": []}
+
+        active = self._active_transaction(state) or {}
+        schema = active.get("parameter_schema") if isinstance(active.get("parameter_schema"), dict) else None
+        if not schema:
+            policy = self._resolve_tool_execution_policy(tool_name, known_arguments or {})
+            schema = self._transaction_parameter_schema(tool_name, policy)
+        description = str(active.get("tool_description") or self._transaction_tool_description(tool_name) or "")
+        route_meta = ((state.get("route_decision") or {}).get("metadata") or {}) if isinstance(state.get("route_decision"), dict) else {}
+        contextual_reentry = bool(route_meta.get("contextual_reentry"))
+        text = (route_meta.get("original_input") if contextual_reentry else None) or state.get("sanitized_input") or state.get("user_text") or ""
+        conversational_context = route_meta.get("relevant_conversation_context") if contextual_reentry else None
+        if not str(conversational_context or "").strip():
+            conversational_context = active.get("parameter_conversational_context")
+        if contextual_reentry and not str(conversational_context or "").strip():
+            effective = str(route_meta.get("contextual_reentry_input") or "")
+            prefix = "CONTEXTO DA SOLICITAÇÃO IMEDIATAMENTE ANTERIOR:\n"
+            suffix = "\n\nCONTINUAÇÃO ATUAL DO CLIENTE:\n"
+            if prefix in effective and suffix in effective:
+                conversational_context = effective.split(prefix, 1)[1].split(suffix, 1)[0].strip()
+        ordered_context = self._transaction_context_newest_first(conversational_context)
+        cached = route_meta.get("transaction_parameter_values")
+        effective_known = dict(known_arguments or {})
+        if isinstance(cached, dict):
+            for key, value in cached.items():
+                if str(key) in set(str(x) for x in parameter_names) and value not in _EMPTY_VALUES:
+                    effective_known[str(key)] = value
+        return await reconcile_transaction_parameters(
+            getattr(self, "llm", None),
+            text=str(text),
+            tool_name=tool_name,
+            parameter_names=[str(x) for x in parameter_names],
+            known_arguments=effective_known,
+            parameter_schema=schema,
+            tool_description=description,
+            conversational_context=ordered_context,
+        )
+
+    async def _extract_transaction_parameters_current_only(
+        self,
+        state: dict[str, Any],
+        *,
+        tool_name: str,
+        missing_parameters: list[str],
+        known_arguments: dict[str, Any] | None = None,
+    ) -> dict[str, Any]:
+        """Collect pending parameters from the current utterance only.
+
+        This is the normal COLLECTING_PARAMETERS path. Temporal conversation
+        history is deliberately excluded here; `_reconcile_transaction_parameters`
+        is the fallback when this direct collection cannot satisfy or validate the
+        declarative parameter contract.
+        """
+        if not missing_parameters:
+            return {}
+
+        # Preserve the framework extension contract: domain/test runtimes that
+        # explicitly override the legacy collector remain the authoritative
+        # implementation of the *traditional* first pass. The temporal fallback
+        # is still owned by the base runtime and is invoked only after failure.
+        override = getattr(type(self), "_extract_transaction_parameters", None)
+        if override is not None and override is not AgentRuntimeMixin._extract_transaction_parameters:
+            return await override(
+                self,
+                state,
+                tool_name=tool_name,
+                missing_parameters=list(missing_parameters),
+                known_arguments=dict(known_arguments or {}),
+            )
+
+        active = self._active_transaction(state) or {}
+        schema = active.get("parameter_schema") if isinstance(active.get("parameter_schema"), dict) else None
+        if not schema:
+            policy = self._resolve_tool_execution_policy(tool_name, known_arguments or {})
+            schema = self._transaction_parameter_schema(tool_name, policy)
+        description = str(active.get("tool_description") or self._transaction_tool_description(tool_name) or "")
+        route_meta = ((state.get("route_decision") or {}).get("metadata") or {}) if isinstance(state.get("route_decision"), dict) else {}
+        text = route_meta.get("original_input") if route_meta.get("contextual_reentry") else None
+        text = text or state.get("sanitized_input") or state.get("user_text") or ""
+        return await extract_transaction_parameters(
+            getattr(self, "llm", None),
+            text=str(text),
+            tool_name=tool_name,
+            missing_parameters=[str(x) for x in missing_parameters],
+            known_arguments=dict(known_arguments or {}),
+            parameter_schema=schema,
+            tool_description=description,
+            conversational_context="",
+        )
+
     async def _extract_transaction_parameters(
         self,
         state: dict[str, Any],
@@ -1535,6 +1682,7 @@ class AgentRuntimeMixin:
             suffix = "\n\nCONTINUAÇÃO ATUAL DO CLIENTE:\n"
             if prefix in effective and suffix in effective:
                 conversational_context = effective.split(prefix, 1)[1].split(suffix, 1)[0].strip()
+        ordered_context = self._transaction_context_newest_first(conversational_context)
         extracted = await extract_transaction_parameters(
             getattr(self, "llm", None),
             text=str(text),
@@ -1543,9 +1691,42 @@ class AgentRuntimeMixin:
             known_arguments={**dict(known_arguments or {}), **reused},
             parameter_schema=schema,
             tool_description=description,
-            conversational_context=str(conversational_context or ""),
+            conversational_context=ordered_context,
         )
-        return {**reused, **extracted}
+
+        # Compatibility/resilience fallback: if a provider returns only a partial
+        # flat extraction, scan bounded prior text source-by-source from newest to
+        # oldest for still-unresolved fields.  This preserves temporal order and
+        # remains schema-driven; the main COLLECTING_PARAMETERS path uses the
+        # coherent set reconciler above.
+        combined = {**reused, **extracted}
+        unresolved = [name for name in missing_parameters if name not in combined]
+        if unresolved and str(conversational_context or "").strip():
+            source_lines = self._transaction_context_newest_first(conversational_context).splitlines()
+            for source_line in source_lines:
+                if not unresolved:
+                    break
+                source_text = source_line.split(": ", 1)[1] if ": " in source_line else source_line
+                low_source = source_text.lower()
+                for role_prefix in ("user: ", "assistant: ", "cliente: ", "agente: ", "system: "):
+                    if low_source.startswith(role_prefix):
+                        source_text = source_text[len(role_prefix):]
+                        break
+                recovered = await extract_transaction_parameters(
+                    getattr(self, "llm", None),
+                    text=source_text,
+                    tool_name=tool_name,
+                    missing_parameters=list(unresolved),
+                    known_arguments={**dict(known_arguments or {}), **combined},
+                    parameter_schema=schema,
+                    tool_description=description,
+                    conversational_context="",
+                )
+                for key, value in recovered.items():
+                    if key in unresolved and value not in _EMPTY_VALUES:
+                        combined[key] = value
+                unresolved = [name for name in unresolved if name not in combined]
+        return combined
 
     def _transactional_action_match(self, text: str, tools: list[str] | None = None) -> str | None:
         """Detecta solicitação transacional usando metadados de tools.yaml.
@@ -2827,23 +3008,48 @@ class AgentRuntimeMixin:
                 policy = self._resolve_tool_execution_policy(tool_name, previous_args)
                 missing_before = self._missing_required_arguments(policy, previous_args)
 
-                # Parâmetros TRANSACIONAIS são interpretados exclusivamente pelo
-                # extrator LLM genérico. Durante COLLECTING_PARAMETERS, a fala atual
-                # também pode CORRIGIR um required field já coletado em turno anterior
-                # (ex.: valor=19,99 e o cliente diz "desculpa, é 14,99" enquanto
-                # subject ainda está pendente). Por isso o contrato editável do turno
-                # é o conjunto completo de ``requires``; somente as chaves realmente
-                # extraídas pela LLM sobrescrevem ``previous_args``. Campos não citados
-                # permanecem intactos. Isso preserva parameter-before-intent-shift sem
-                # tornar valores antigos imutáveis por acidente.
-                editable_required = [str(name) for name in (policy.get("requires") or [])]
-                extracted = await self._extract_transaction_parameters(
+                # Primeira tentativa: coleta tradicional usando SOMENTE a fala
+                # atual. O histórico não deve participar enquanto a mensagem corrente
+                # conseguir preencher o contrato normalmente.
+                arguments = dict(previous_args)
+                extracted_current = await self._extract_transaction_parameters_current_only(
                     state,
                     tool_name=tool_name,
-                    missing_parameters=editable_required,
+                    missing_parameters=missing_before,
                     known_arguments=previous_args,
                 )
-                arguments = {**previous_args, **extracted}
+                arguments.update(dict(extracted_current or {}))
+                state["transaction_parameter_collection"] = {
+                    "tool_name": tool_name,
+                    "mode": "current_turn",
+                    "resolved_fields": sorted(str(k) for k in (extracted_current or {}).keys()),
+                }
+
+                # Segunda opção: somente se a coleta tradicional ainda deixar
+                # required fields em aberto, reconcilie temporalmente usando o
+                # schema/descrições declarados em tools.yaml e o contexto bounded
+                # newest->oldest preservado na transação.
+                missing_after_current = self._missing_required_arguments(policy, arguments)
+                if missing_after_current:
+                    required_fields = [str(name) for name in (policy.get("requires") or [])]
+                    reconciliation = await self._reconcile_transaction_parameters(
+                        state,
+                        tool_name=tool_name,
+                        parameter_names=required_fields,
+                        known_arguments=arguments,
+                    )
+                    for field in reconciliation.get("clear_fields") or []:
+                        arguments.pop(str(field), None)
+                    arguments.update(dict(reconciliation.get("values") or {}))
+                    state["transaction_parameter_reconciliation"] = {
+                        "tool_name": tool_name,
+                        "trigger": "traditional_collection_unresolved",
+                        "decisions": dict(reconciliation.get("decisions") or {}),
+                        "provenance": dict(reconciliation.get("provenance") or {}),
+                        "clear_fields": list(reconciliation.get("clear_fields") or []),
+                    }
+                else:
+                    state.pop("transaction_parameter_reconciliation", None)
 
                 # Argumentos estruturados já presentes no contexto são aceitos de
                 # forma genérica (não são parsing textual). Para required fields,
@@ -2889,6 +3095,64 @@ class AgentRuntimeMixin:
                 pre_validation_result = await self._run_transaction_pre_validation(
                     state, tool_name=tool_name, arguments=arguments, policy=policy, emit_events=emit_events
                 )
+                if pre_validation_result is not None:
+                    # A pre-validation é a fronteira autoritativa da coleta
+                    # tradicional. Se ela rejeitar um candidato de forma recuperável
+                    # (NEEDS_PARAMETER), ainda não repromptamos o usuário: fazemos UMA
+                    # tentativa de reconciliação temporal com o histórico bounded e
+                    # com a semântica declarada em tools.yaml. Isso cobre referências
+                    # como "é a de R$ 19,99" sem tornar o reconciliador o caminho
+                    # primário de COLLECTING_PARAMETERS.
+                    pre_meta = state.get("transaction_pre_validation")
+                    pre_meta = pre_meta if isinstance(pre_meta, dict) else {}
+                    retry_parameter = str(pre_meta.get("parameter") or "").strip()
+                    recoverable = (
+                        str(pre_meta.get("status") or "").upper() == "NEEDS_PARAMETER"
+                        and bool(retry_parameter)
+                        and state.get("transaction_status") == "COLLECTING_PARAMETERS"
+                    )
+                    if recoverable:
+                        recovered_selected = dict(self._active_transaction(state) or state.get("selected_tool_call") or {})
+                        recovered_arguments = dict(recovered_selected.get("arguments") or {})
+                        required_fields = [str(name) for name in (policy.get("requires") or [])]
+                        reconciliation = await self._reconcile_transaction_parameters(
+                            state,
+                            tool_name=tool_name,
+                            parameter_names=required_fields,
+                            known_arguments=recovered_arguments,
+                        )
+                        for field in reconciliation.get("clear_fields") or []:
+                            recovered_arguments.pop(str(field), None)
+                        recovered_arguments.update(dict(reconciliation.get("values") or {}))
+                        state["transaction_parameter_reconciliation"] = {
+                            "tool_name": tool_name,
+                            "trigger": "prevalidation_needs_parameter",
+                            "rejected_parameter": retry_parameter,
+                            "decisions": dict(reconciliation.get("decisions") or {}),
+                            "provenance": dict(reconciliation.get("provenance") or {}),
+                            "clear_fields": list(reconciliation.get("clear_fields") or []),
+                        }
+                        retry_policy = self._resolve_tool_execution_policy(tool_name, recovered_arguments)
+                        retry_missing = self._missing_required_arguments(retry_policy, recovered_arguments)
+                        if retry_parameter not in retry_missing:
+                            state["selected_tool_call"] = {"tool_name": tool_name, "arguments": recovered_arguments}
+                            self._set_active_transaction(
+                                state, tool_name=tool_name, arguments=recovered_arguments, status="COLLECTING_PARAMETERS"
+                            )
+                            state["missing_parameters"] = retry_missing
+                            pre_validation_retry = await self._run_transaction_pre_validation(
+                                state,
+                                tool_name=tool_name,
+                                arguments=recovered_arguments,
+                                policy=retry_policy,
+                                emit_events=emit_events,
+                            )
+                            if pre_validation_retry is None:
+                                arguments = recovered_arguments
+                                policy = retry_policy
+                                pre_validation_result = None
+                            else:
+                                return [pre_validation_retry]
                     if pre_validation_result is not None:
                         return [pre_validation_result]
 

agent_framework_oci/libs/agent_framework/src/agent_framework/runtime/transaction_parameters.py

@@ -69,69 +69,77 @@ def parse_transaction_confirmation(text: str) -> str | None:
     return None
 
 
-async def extract_transaction_parameters(
+async def reconcile_transaction_parameters(
     llm: Any,
     *,
     text: str,
     tool_name: str,
-    missing_parameters: list[str],
+    parameter_names: list[str],
     known_arguments: Mapping[str, Any] | None = None,
     parameter_schema: Mapping[str, Any] | None = None,
     tool_description: str | None = None,
     conversational_context: str | None = None,
 ) -> dict[str, Any]:
-    """Extract values for pending transactional parameters using the LLM only.
+    """Rebuild a coherent parameter set from text, newest to oldest.
 
-    This component intentionally contains no domain/entity regexes and no
-    knowledge of parameter names such as ``order_id`` or ``reason``.  The
-    transaction runtime supplies the pending parameter names and optional schema;
-    the LLM only interprets the current user turn.  State/control-flow decisions
-    remain deterministic outside this function.
+    The framework is intentionally field/domain neutral.  Meaning comes from the
+    declarative tool schema (normally tools.yaml) plus the conversation text.
+    The LLM may resolve a field, preserve a previously known value, explicitly
+    clear a stale value whose context was superseded, or leave a field unresolved.
+    Returned provenance is interpretive metadata only; authoritative business
+    validation still belongs to the configured pre-validation/tool layer.
     """
-    pending = [str(name) for name in (missing_parameters or []) if str(name).strip()]
+    names = [str(name) for name in (parameter_names or []) if str(name).strip()]
     message = str(text or "").strip()
-    if not pending or not message or llm is None:
-        return {}
+    if not names or not message or llm is None:
+        return {"values": {}, "decisions": {}, "provenance": {}, "clear_fields": []}
 
     schema = dict(parameter_schema or {})
     known = {
         str(key): value
         for key, value in dict(known_arguments or {}).items()
-        if value not in _EMPTY_VALUES and str(key) not in pending
+        if value not in _EMPTY_VALUES
     }
     field_spec = {
         name: {
             "type": schema.get(name, "string") if not isinstance(schema.get(name), dict) else schema.get(name, {}).get("type", "string"),
             "description": None if not isinstance(schema.get(name), dict) else schema.get(name, {}).get("description"),
         }
-        for name in pending
+        for name in names
+    }
+    output_shape = {
+        "fields": {
+            name: {"decision": "resolved|preserve|clear|unresolved", "value": None, "source": "current|history:N|state"}
+            for name in names
+        }
     }
-    output_shape = {name: None for name in pending}
     prompt = (
-        "Você extrai parâmetros PENDENTES de uma transação ativa. "
-        "Sua única tarefa é interpretar a mensagem atual e devolver valores para os parâmetros pendentes. "
-        "Não decida roteamento, intenção, confirmação ou execução da transação.\n\n"
-        "REGRAS OBRIGATÓRIAS:\n"
-        "1. Extraia SOMENTE parâmetros listados em pending_parameters.\n"
-        "2. Não invente valores e não transforme uma nova solicitação/intenção do usuário em valor de parâmetro.\n"
-        "3. Se nenhum parâmetro pendente foi realmente informado, devolva null para todos.\n"
-        "4. Se houver apenas um parâmetro pendente, uma resposta contendo apenas um valor pode ser associada a ele quando isso for semanticamente inequívoco.\n"
-        "5. Se houver vários parâmetros pendentes, extraia todos os que estiverem presentes no mesmo turno.\n"
-        "6. O nome do parâmetro não precisa aparecer literalmente na fala. Associe semanticamente o valor usando o nome da transação e os metadados disponíveis para cada campo.\n"
-        "7. Para cada parâmetro, considere o nome técnico, o tipo quando disponível e principalmente a descrição semântica quando disponível. A ausência de tipo ou descrição NÃO impede a extração.\n"
-        "8. Se a mensagem deixar clara a correspondência entre um trecho e um parâmetro, preencha-o mesmo que o usuário não cite o nome técnico do campo.\n"
-        "9. Não use conhecimento externo para completar valores ausentes e não transforme aproximações ou suposições em fatos.\n"
-        "10. conversational_context, quando presente, serve SOMENTE para resolver referências da mensagem atual (por exemplo: 'a de 14,99' apontando para um item citado imediatamente antes). Não trate texto do contexto como uma nova afirmação do cliente nem como evidência de negócio.\n"
-        "11. Quando a mensagem atual identifica um valor OU nome e o contexto imediatamente anterior contém uma única entidade compatível, você pode preencher essa entidade e os atributos pendentes inequivocamente associados a ela como CANDIDATOS. Exemplo genérico: se a fala identifica uma entidade e o contexto associa unicamente essa entidade a um valor requerido, o valor pode ser retornado como candidato. A validação autoritativa ocorrerá depois; não invente se houver ambiguidade.\n"
-        "12. Em caso de dúvida razoável sobre a correspondência ou o valor, prefira null.\n"
-        "13. Responda SOMENTE JSON válido, sem markdown, sem explicação e sem chaves extras.\n\n"
+        "Você é o conciliador temporal de parâmetros de uma transação ativa. "
+        "Reconstrua UM CONJUNTO COERENTE de parâmetros; não extraia cada campo de forma isolada. "
+        "Não decida roteamento, confirmação nem execução.\n\n"
+        "CONTRATO OBRIGATÓRIO:\n"
+        "1. O significado de cada campo vem EXCLUSIVAMENTE de parameter_schema e transaction_description, considerando principalmente a descrição semântica quando disponível. O framework não conhece conceitos de domínio por nome de campo.\n"
+        "2. A ausência de tipo ou descrição NÃO impede a extração quando o restante do contrato e o texto forem semanticamente suficientes.\n"
+        "3. A busca textual é temporal, em ordem decrescente: user_message é a fonte mais nova; depois conversational_context já vem do texto mais novo para o mais antigo.\n"
+        "3. Textos do usuário e textos explicativos do assistente podem ser usados para interpretar referências e relações entre campos. Não trate texto do contexto como uma nova afirmação do cliente; eles são contexto, não evidência autoritativa de negócio.\n"
+        "4. Para cada campo escolha: resolved = um texto determina novo valor; preserve = nenhum texto mais novo invalida o valor conhecido; clear = existe mudança textual mais nova que torna o valor conhecido incompatível/sem vínculo seguro com o novo contexto e ainda não há substituto; unresolved = não há valor conhecido nem candidato textual seguro.\n"
+        "5. Se uma fonte mais nova muda uma entidade, objeto, escopo ou outro contexto que dava sentido a campos extraídos anteriormente, REAVALIE os demais campos como conjunto. Não combine automaticamente um atributo antigo com um contexto novo só porque ambos existem.\n"
+        "6. Um valor de texto anterior pode continuar válido após uma mudança somente quando os textos, considerados em conjunto, sustentarem de forma inequívoca que ele ainda pertence ao contexto mais recente. Caso contrário use clear para o campo dependente.\n"
+        "7. Se a nova menção é inválida no mundo real, NÃO volte silenciosamente ao valor antigo. Ainda assim devolva o candidato textual mais recente como resolved; a pre-validation autoritativa é responsável por rejeitá-lo.\n"
+        "8. known_arguments é fallback de estado. Use preserve somente quando nenhum texto mais novo o contradiz ou rompe sua associação contextual.\n"
+        "9. Uma expressão só pode preencher um campo se satisfizer semanticamente type/description desse campo. Não transfira um trecho para outro campo apenas por proximidade lexical.\n"
+        "10. Se um texto recente corrige explicitamente informação anterior, a correção prevalece para os campos que o schema permite inferir; os demais devem ser reavaliados quanto à coerência com a correção.\n"
+        "11. Em ambiguidade razoável, prefira clear/unresolved a inventar uma associação. Em caso de dúvida razoável sobre a correspondência ou o valor, prefira null.\n"
+        "12. Responda SOMENTE JSON válido no formato pedido, sem markdown e sem chaves extras.\n\n"
         f"transaction_tool: {tool_name}\n"
         f"transaction_description: {tool_description or ''}\n"
-        f"pending_parameters: {json.dumps(pending, ensure_ascii=False)}\n"
+        f"parameter_names: {json.dumps(names, ensure_ascii=False)}\n"
+        f"pending_parameters: {json.dumps(names, ensure_ascii=False)}\n"
         f"parameter_schema: {json.dumps(field_spec, ensure_ascii=False, default=str)}\n"
         f"known_arguments: {json.dumps(known, ensure_ascii=False, default=str)}\n"
-        f"conversational_context: {str(conversational_context or '').strip()}\n"
+        "conversation_sources_newest_to_oldest:\n"
         f"user_message: {message}\n"
+        f"conversational_context: {str(conversational_context or '').strip()}\n"
         f"Formato obrigatório: {json.dumps(output_shape, ensure_ascii=False)}"
     )
 
@@ -144,44 +152,98 @@ async def extract_transaction_parameters(
             temperature=0.0,
         )
     except TypeError:
-        # Compatibilidade com doubles/testes e providers mínimos que aceitam
-        # apenas messages.
         response = await llm.ainvoke([{"role": "user", "content": prompt}])
     except Exception as exc:
-        logger.warning(
-            "transaction.parameter.llm_extract_failed tool=%s pending=%s error=%s",
-            tool_name,
-            pending,
-            exc,
-        )
-        return {}
+        logger.warning("transaction.parameter.reconcile_failed tool=%s fields=%s error=%s", tool_name, names, exc)
+        return {"values": {}, "decisions": {}, "provenance": {}, "clear_fields": []}
 
     raw = _response_text(response).strip()
     try:
         payload = parse_json_object(raw)
     except (TypeError, ValueError):
-        logger.warning(
-            "transaction.parameter.llm_invalid_structured_output tool=%s pending=%s raw=%r",
-            tool_name,
-            pending,
-            raw[:240],
-        )
-        return {}
+        logger.warning("transaction.parameter.reconcile_invalid_output tool=%s raw=%r", tool_name, raw[:240])
+        return {"values": {}, "decisions": {}, "provenance": {}, "clear_fields": []}
     if not isinstance(payload, dict):
-        return {}
+        return {"values": {}, "decisions": {}, "provenance": {}, "clear_fields": []}
 
-    extracted: dict[str, Any] = {}
-    for name in pending:
-        value = payload.get(name)
+    # Backward compatibility with existing providers/test doubles that return a
+    # flat {field: value} object. Non-null flat values mean ``resolved``.
+    fields = payload.get("fields") if isinstance(payload.get("fields"), dict) else None
+    if fields is None:
+        fields = {}
+        for name in names:
+            flat_value = payload.get(name)
+            if flat_value in _EMPTY_VALUES:
+                decision = "preserve" if name in known else "unresolved"
+                source = "state" if decision == "preserve" else ""
+            else:
+                decision = "resolved"
+                source = "current"
+            fields[name] = {"decision": decision, "value": flat_value, "source": source}
+
+    values: dict[str, Any] = {}
+    decisions: dict[str, str] = {}
+    provenance: dict[str, str] = {}
+    clear_fields: list[str] = []
+    for name in names:
+        item = fields.get(name) if isinstance(fields, dict) else None
+        if not isinstance(item, dict):
+            item = {"decision": "unresolved", "value": None, "source": ""}
+        decision = str(item.get("decision") or "unresolved").strip().lower()
+        if decision not in {"resolved", "preserve", "clear", "unresolved"}:
+            decision = "unresolved"
+        decisions[name] = decision
+        source = str(item.get("source") or "").strip()
+        if source:
+            provenance[name] = source
+        if decision == "clear":
+            clear_fields.append(name)
+            continue
+        if decision == "preserve":
+            if name in known:
+                values[name] = known[name]
+            continue
+        if decision != "resolved":
+            continue
         declared = field_spec.get(name, {}).get("type", "string")
-        coerced = _coerce(value, declared)
+        coerced = _coerce(item.get("value"), declared)
         if coerced not in _EMPTY_VALUES:
-            extracted[name] = coerced
+            values[name] = coerced
+        else:
+            decisions[name] = "unresolved"
 
     logger.info(
-        "transaction.parameter.llm_extracted tool=%s pending=%s consumed=%s",
-        tool_name,
-        pending,
-        sorted(extracted),
+        "transaction.parameter.reconciled tool=%s decisions=%s resolved=%s clear=%s provenance=%s",
+        tool_name, decisions, sorted(values), clear_fields, provenance,
     )
-    return extracted
+    return {"values": values, "decisions": decisions, "provenance": provenance, "clear_fields": clear_fields}
+
+
+async def extract_transaction_parameters(
+    llm: Any,
+    *,
+    text: str,
+    tool_name: str,
+    missing_parameters: list[str],
+    known_arguments: Mapping[str, Any] | None = None,
+    parameter_schema: Mapping[str, Any] | None = None,
+    tool_description: str | None = None,
+    conversational_context: str | None = None,
+) -> dict[str, Any]:
+    """Compatibility facade returning only resolved candidates.
+
+    New runtime code should use :func:`reconcile_transaction_parameters` when it
+    needs preserve/clear/provenance decisions.  Keeping this facade avoids
+    breaking routers and existing extensions that only need candidate extraction.
+    """
+    result = await reconcile_transaction_parameters(
+        llm,
+        text=text,
+        tool_name=tool_name,
+        parameter_names=missing_parameters,
+        known_arguments=known_arguments,
+        parameter_schema=parameter_schema,
+        tool_description=tool_description,
+        conversational_context=conversational_context,
+    )
+    return dict(result.get("values") or {})

app/agents/suporte_contas_agent.py

@@ -49,6 +49,19 @@ class SuporteContasAgent(AgentRuntimeMixin):
             )
 
         state["mcp_results"] = tool_context
+
+        # A no-match retry is already a resolved control-flow decision: the
+        # framework/router concluded that the utterance is not understood. Do
+        # not ask another generative model to reinterpret it into a concrete
+        # business action (which can fabricate intent from noisy ASR).
+        if str(state.get("intent") or "") == "contas_no_match_retry":
+            return {
+                "answer": "Desculpe, não entendi. Poderia repetir de outra forma?",
+                "next_state": state.get("next_state") or "SUPORTE_CONTAS_ACTIVE",
+                "mcp_results": tool_context,
+                **self.transaction_state_patch(state),
+            }
+
         clarification_message = self.transaction_clarification_message(state)
         if clarification_message:
             return {

app/extensions/tim_guardrails.py

@@ -147,6 +147,43 @@ def _authorized_human_handoff(context: dict[str, Any]) -> bool:
 
     return any(terminal_workflow_handoff(root) for root in roots)
 
+
+
+def _deterministic_tim_domain_response(text: str, context: dict[str, Any]) -> bool:
+    """Conservative fast path for unmistakable TIM Contas domain output.
+
+    This extension may know the TIM Contas vocabulary; the generic framework does
+    not. Human-transfer language is intentionally excluded so handoff authorization
+    remains exclusively structural. Ambiguous domain prose still goes to TIM_OOS LLM.
+    """
+    normalized = re.sub(r'\s+', ' ', str(text or '').strip().lower())
+    if not normalized:
+        return False
+
+    handoff_markers = (
+        'encaminhar', 'encaminhado', 'transferir', 'transferido',
+        'atendente', 'pessoa', 'humano', 'especialista',
+    )
+    if any(m in normalized for m in handoff_markers):
+        return False
+
+    domain_markers = (
+        'contestação', 'contestacao', 'fatura', 'cobrança', 'cobranca',
+        'plano', 'serviço', 'servico', 'vas', 'boleto', 'desconto',
+    )
+    completion_markers = (
+        'registrad', 'cancelad', 'concluíd', 'concluid', 'executad',
+        'processad', 'enviad', 'emitid', 'protocolo',
+    )
+
+    # Keep the bypass intentionally narrow: an unmistakable domain noun plus an
+    # operational/result marker. Ordinary explanations and ambiguous responses are
+    # still semantically audited by TIM_OOS.
+    return (
+        any(m in normalized for m in domain_markers)
+        and any(m in normalized for m in completion_markers)
+    )
+
 def _parse_json(raw: Any) -> dict[str, Any]:
     text = str(getattr(raw, 'content', raw) or '').strip()
     m = re.search(r'\{[\s\S]*\}', text)
@@ -196,6 +233,22 @@ class TimOutOfScopeRail(_TimPromptRail):
                     },
                 },
             )
+        if _deterministic_tim_domain_response(text, context):
+            return RailDecision(
+                code=self.code,
+                allowed=True,
+                reason='resposta suportada pelo contexto do domínio TIM Contas',
+                sanitized_text=text,
+                metadata={
+                    'external': True,
+                    'domain': 'TIM_CONTAS',
+                    'mechanism': 'deterministic_domain_bypass',
+                    'data': {
+                        'allowed': True,
+                        'reason': 'resposta suportada pelo contexto do domínio TIM Contas',
+                    },
+                },
+            )
         return await super().evaluate(text, context)
 
 class TimProactiveOfferRail(_TimPromptRail):
@@ -347,6 +400,8 @@ class TimPrematureActionRail(_TimPromptRail):
                 'final_answer', 'customer_message', 'customer_response', 'answer', 'text',
                 'epistemic_status', 'output', 'result', 'items', 'results', 'errors',
                 'cancelados', 'nao_cancelados', 'nao_encontrados', 'terminal_status',
+                'contestation_registered', 'contested_invoice_amount', 'contested_invoice_amount_open',
+                'sms_sent', 'barcode', 'itemName', 'itemsResponse', 'contested_items', 'sr',
             }
             out = {}
             for k, v in value.items():
@@ -414,6 +469,142 @@ class TimPrematureActionRail(_TimPromptRail):
             return False
         return candidate in cls._authoritative_output_messages(context)
 
+    @staticmethod
+    def _normalized_text(value: Any) -> str:
+        text = str(value or '').lower()
+        text = text.replace('r$', ' ')
+        text = re.sub(r'\s+', ' ', text)
+        return text.strip()
+
+    @classmethod
+    def _candidate_has_operational_completion_claim(cls, text: str) -> bool:
+        """Detect completion/effect claims, not ordinary explanations.
+
+        REVPREC exists to stop claims that an operation already happened.  A
+        billing explanation containing values/dates is not such a claim and must
+        not depend on a probabilistic binary LLM classification.
+        """
+        normalized = cls._normalized_text(text)
+        patterns = (
+            r'\b(cancelad[oa]s?|cancelei|cancelamos|cancelou|cancelamento .*conclu)',
+            r'\b(contesta(?:ção|cao) .*?(?:registrad|abert|conclu|criad))',
+            r'\b(registrad[oa]s?|executei|executamos|executou|executad[oa]s?|conclu[ií]d[oa]s?|processad[oa]s?)\b',
+            r'\b(enviad[oa]s?|emitid[oa]s?|retirad[oa]s?|removid[oa]s?)\b',
+            r'\b(protocol(?:o)? .*?(?:abert|gerad|registrad))',
+            r'\b(?:foi|foram) (?:cancelad|contestad|registrad|enviad|emitid|retirad|removid)',
+            r'\b(?:was|were|has been) (?:cancelled|canceled|registered|sent|issued|removed)',
+        )
+        return any(re.search(pattern, normalized, re.I) for pattern in patterns)
+
+    @classmethod
+    def _current_execution_scalar_evidence(cls, context: dict[str, Any]) -> tuple[list[str], bool, bool]:
+        """Flatten scalar facts from current successful result/output only.
+
+        Returns (facts, completed, positive_effect).  Inputs, conversation state,
+        history and metadata are excluded so user claims can never prove an action.
+        """
+        roots = (context or {}).get('mcp_results')
+        if roots is None:
+            roots = (context or {}).get('tool_result')
+        if isinstance(roots, dict):
+            roots = [roots]
+        if not isinstance(roots, (list, tuple)):
+            return [], False, False
+
+        facts: list[str] = []
+        completed = False
+        positive = False
+        positive_status = {'completed', 'opened', 'open', 'iniciada', 'iniciado', 'success', 'succeeded', 'closed', 'fechado'}
+        positive_keys = {
+            'success', 'executed', 'contestation_registered', 'sms_sent',
+            'cancelled', 'canceled', 'registered', 'created', 'updated',
+        }
+        skip = {'state', 'input', 'metadata', 'conversation_history', 'history', 'session', 'session_metadata'}
+
+        def walk(value: Any, path: tuple[str, ...] = ()) -> None:
+            nonlocal completed, positive
+            if isinstance(value, dict):
+                for k, v in value.items():
+                    key = str(k).lower()
+                    if key in skip:
+                        continue
+                    if key == 'status' and str(v).strip().lower() == 'completed':
+                        completed = True
+                    if key in positive_keys and v is True:
+                        positive = True
+                    if key == 'status' and str(v).strip().lower() in positive_status:
+                        positive = True
+                    # Keys such as sms_sent/barcode are themselves meaningful
+                    # evidence and can support paraphrased customer-facing text.
+                    if key not in {'output', 'result', 'results'}:
+                        facts.append(key)
+                    walk(v, path + (key,))
+            elif isinstance(value, (list, tuple)):
+                for item in value[:50]:
+                    walk(item, path)
+            elif value not in (None, ''):
+                facts.append(str(value))
+
+        for item in roots:
+            if not isinstance(item, dict) or item.get('ok') is False:
+                continue
+            if str(((item.get('result') or {}) if isinstance(item.get('result'), dict) else {}).get('status') or '').upper() == 'COMPLETED':
+                completed = True
+            walk(item.get('result', item))
+        return facts, completed, positive
+
+    @classmethod
+    def _candidate_is_structurally_grounded_completion(cls, text: str, context: dict[str, Any]) -> bool:
+        """Allow a paraphrased completion only when current execution proves it.
+
+        This deliberately does not know tool names.  It requires a completed
+        current execution, a positive side-effect marker, grounding for every
+        material number mentioned by the candidate, and at least one meaningful
+        textual fact/entity shared with the execution result.
+        """
+        if not cls._candidate_has_operational_completion_claim(text):
+            return False
+        facts, completed, positive = cls._current_execution_scalar_evidence(context)
+        if not (completed and positive and facts):
+            return False
+
+        candidate = cls._normalized_text(text)
+        evidence_text = cls._normalized_text(' '.join(facts))
+
+        def canon_number(raw: str) -> str:
+            raw = raw.strip().replace(' ', '')
+            if ',' in raw and '.' in raw:
+                if raw.rfind(',') > raw.rfind('.'):
+                    raw = raw.replace('.', '').replace(',', '.')
+                else:
+                    raw = raw.replace(',', '')
+            else:
+                raw = raw.replace(',', '.')
+            try:
+                num = float(raw)
+                return (f'{num:.6f}').rstrip('0').rstrip('.')
+            except ValueError:
+                return re.sub(r'\D', '', raw)
+
+        candidate_numbers = [canon_number(x) for x in re.findall(r'(?<!\w)\d[\d .,-]*\d|(?<!\w)\d', candidate)]
+        evidence_numbers = {canon_number(x) for x in re.findall(r'(?<!\w)\d[\d .,-]*\d|(?<!\w)\d', evidence_text)}
+        material_numbers = [n for n in candidate_numbers if len(re.sub(r'\D', '', n)) >= 2]
+        if not material_numbers:
+            return False
+        if any(n not in evidence_numbers for n in material_numbers):
+            return False
+
+        # Require an entity/message overlap beyond generic success vocabulary.
+        candidate_words = set(re.findall(r'[a-zà-ÿ0-9+_-]{4,}', candidate, re.I))
+        stop = {
+            'sucesso', 'registrada', 'registrado', 'concluido', 'concluida',
+            'cliente', 'valor', 'protocolo', 'cobranca', 'contestacao', 'servico',
+            'mensal', 'novo', 'boleto', 'recebeu', 'detalhes',
+        }
+        evidence_words = set(re.findall(r'[a-zà-ÿ0-9+_-]{4,}', evidence_text, re.I))
+        meaningful_overlap = (candidate_words - stop) & (evidence_words - stop)
+        return bool(meaningful_overlap)
+
     async def evaluate(self, text: str, context: dict[str, Any]) -> RailDecision:
         # Strongest possible proof: the exact customer-facing candidate was emitted by
         # the successful current-turn tool/workflow itself.  REVPREC is about premature
@@ -433,6 +624,41 @@ class TimPrematureActionRail(_TimPromptRail):
                 },
             )
 
+        # REVPREC is not a general factuality rail. Ordinary descriptions and
+        # explanations contain no claim that an operation has already completed,
+        # so they are deterministically outside this rail's blocking scope.
+        if not self._candidate_has_operational_completion_claim(text):
+            return RailDecision(
+                code=self.code,
+                allowed=True,
+                reason='no_operational_completion_claim',
+                sanitized_text=text,
+                metadata={
+                    'external': True,
+                    'domain': 'TIM_CONTAS',
+                    'mechanism': 'deterministic_claim_scope',
+                    'terminal_action': 'retry',
+                },
+            )
+
+        # A composed/paraphrased response can still be proven by structured
+        # current-turn execution evidence even when it is not byte-for-byte equal
+        # to a workflow message. This removes nondeterministic false positives
+        # while keeping unsupported or contradictory claims on the semantic path.
+        if self._candidate_is_structurally_grounded_completion(text, context):
+            return RailDecision(
+                code=self.code,
+                allowed=True,
+                reason='current_execution_structurally_grounded',
+                sanitized_text=text,
+                metadata={
+                    'external': True,
+                    'domain': 'TIM_CONTAS',
+                    'mechanism': 'deterministic_structured_execution_evidence',
+                    'terminal_action': 'retry',
+                },
+            )
+
         evidence = self._current_execution_evidence(context)
         llm = _llm(context)
         if llm is None: