Building AI Agents with LangChain and OpenAI

AI agents loop: observe, plan, act with tools, observe again. They handle multi-step tasks like research, booking, or code changes.

Tool Definition

from langchain.tools import tool

@tool
def search_docs(query: str) -> str:
    '''Search internal documentation.'''
    return vector_store.similarity_search(query, k=5)

Bind tools to the model; the model returns tool calls you execute and feed back as observations.

Control and Safety

Set max iterations. Require human approval for destructive tools. Log every step for audit.

Conclusion

Agents are powerful and unpredictable. Start with a fixed workflow (chain); graduate to agents when the task path genuinely varies per request.