Autonomous AI Agent Development Services

from typing import TypedDict, Annotated, Sequence
import operator
from langgraph.graph import StateGraph, END
from langgraph.checkpoint.postgres import PostgresSaver
from langchain_core.messages import BaseMessage, HumanMessage, SystemMessage

class AgentState(TypedDict):
  messages: Annotated[Sequence[BaseMessage], operator.add]
  retry_count: int
  execution_status: str

def plan_step(state: AgentState) -> dict:
  # Evaluate goal and determine required tool call
  return {"execution_status": "PLANNING"}

def execute_tool_step(state: AgentState) -> dict:
  if state.get("retry_count", 0) > 3:
      return {"execution_status": "FAILED_CIRCUIT_BREAK"}
  return {"execution_status": "COMPLETED", "retry_count": state.get("retry_count", 0) + 1}

def route_next_step(state: AgentState) -> str:
  if state["execution_status"] == "FAILED_CIRCUIT_BREAK":
      return "human_escalation"
  if state["execution_status"] == "COMPLETED":
      return END
  return "execute_tool"

# Construct state graph
workflow = StateGraph(AgentState)
workflow.add_node("planner", plan_step)
workflow.add_node("execute_tool", execute_tool_step)
workflow.set_entry_point("planner")
workflow.add_conditional_edges("planner", route_next_step)

# Compile with persistent PostgreSQL checkpointer
# app = workflow.compile(checkpointer=PostgresSaver(conn_string))