Building Resilient Autonomous AI Agent Workflows: Combining LangChain, n8n, and Vector Databases

import json
import logging
from pydantic import BaseModel, ValidationError
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

class StructuredAction(BaseModel):
    tool_name: str
    target_endpoint: str
    payload: dict
    confidence_score: float

# Initialize LLM with strict JSON schema enforcement
llm = ChatOpenAI(model="gpt-4o", temperature=0.0)
structured_llm = llm.with_structured_output(StructuredAction)

async def execute_agent_step_with_retry(user_intent: str, max_retries: int = 3) -> StructuredAction:
    attempts = 0
    last_error = ""
    
    while attempts < max_retries:
        try:
            prompt = f"Plan action for intent: {user_intent}. Previous Error: {last_error}"
            response = await structured_llm.ainvoke(prompt)
            
            # Additional business rule assertion
            if response.confidence_score < 0.85:
                raise ValueError(f"Confidence score {response.confidence_score} below threshold 0.85")
                
            return response
        except (ValidationError, ValueError) as e:
            attempts += 1
            last_error = str(e)
            logging.warning(f"Agent step failed attempt {attempts}/{max_retries}: {last_error}")
            
    raise RuntimeError(f"Agent failed after {max_retries} retries. Escalate to human operator.")