Worked on the pydantic/pydantic-ai repository to enhance the reliability of AI agent orchestration by addressing a key robustness issue in model response handling. Developed and implemented a retry mechanism in Python that detects when the agent model produces only “thinking” outputs without actionable content, prompting the model to regenerate a more complete response. This approach reduced the frequency of incomplete outputs and minimized downstream failures, supporting higher uptime for automated systems. The work demonstrated skills in AI agent development, LLM integration, and robust error handling, with clear traceability through targeted bug fixes and thoughtful improvements to automation workflows.