Projeto do Agent Contas ORACLE

app/state.py

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