AI Agents and Workflows
Self-Correcting Agent - The Robust Reasoning Loop
Build a self-correcting ReAct agent that catches tool errors, feeds them back to the LLM, and retries automatically. Error recovery with LangGraph.
Your agent calls a tool with the wrong argument. The tool raises an exception. The application crashes.
This happens constantly in production. An LLM asks for "Price-to-Earnings" when the API expects "P/E". It invents a ticker symbol that doesn't exist. It passes a string where the function expects an integer. Standard ReAct implementations treat these failures as fatal. They shouldn't be.
The fix: catch the exception, format the error as a ToolMessage, and send it back to the LLM. The model reads the error, adjusts its arguments, and retries. Most corrections land on the second attempt.
What You'll Build
- A stock screening agent with strict input validation
- Error recovery via try/except in LangGraph nodes
- Python exceptions formatted as ToolMessage for the LLM
- Max retry limits to prevent infinite error loops