Taming Rogue AI Agents with Observability-Driven Evaluation — Jim Bennett, Galileo
Jun 27, 2025 · 16:14
Jim Bennett, Principal Developer Advocate at Galileo, argues that AI agents must be tamed using observability-driven evaluation, where LLMs evaluate other LLMs to detect failures like hallucinations and tool misuse. He cites real-world examples: the Chicago Sun-Times publishing a hallucinated summer reading list, and a lawyer citing false AI-generated case law. Bennett demonstrates a fintech chatbot that fails to answer 'what is my account balance' directly, requiring three turns; metrics like 'action completion' and 'action advancement' reveal the agent advances but does not complete. He stresses granular evaluation at every step—LLM calls, tool use, RAG retrieval—and using a better, custom-trained LLM as the evaluator. Human feedback is essential to correct mis-scored metrics and continuously retrain. Bennett urges adding evaluations from day one, even before production, and maintaining them in CI/CD and production with alerting for rogue behavior.
