A comprehensive guide to LLM evaluation and AI observability for production AI agents. Covers core evaluation metrics including hallucination rate, toxicity scores, and frameworks like DeepEval and RAGAS. Explains why traditional LLM evaluation falls short for multi-step agents and introduces advanced metrics such as task completion rate, tool usage correctness, step-by-step reasoning accuracy, and groundedness. Also covers AI observability concepts, popular monitoring tools (LangSmith, LangFuse, OpenLLMetry, Phoenix, TruLens, Datadog), and best practices including cost/latency monitoring, combining offline and online evaluation, and human-in-the-loop oversight. PyCharm's AI Agents Debugger and Hugging Face integration are highlighted as practical tools for implementing these workflows.
