An engineer built a self-healing data pipeline to handle unpredictable data sources. When a `pandas.read_csv()` call fails, the system catches the exception and sends the traceback and a file snippet to an LLM. The model diagnoses the issue—like a delimiter mismatch or encoding problem—and returns structured parameters for a retry, eliminating late-night fixes.

The architecture uses a "Try-Heal-Retry" loop. Key tools include Pydantic to enforce a strict JSON schema on the LLM's output, preventing conversational noise. Tenacity manages the retry logic, calling a custom healer function between attempts. This setup transforms a brittle pipeline into one that adapts to schema changes without human intervention.

This approach offers a practical middle ground between full automation and manual oversight. It's particularly useful for teams dealing with unreliable third-party data feeds. While not a silver bullet for all errors, it demonstrates how targeted agentic AI can handle repetitive debugging tasks, freeing engineers from mundane failures and improving system resilience.