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Monitoring Agents in Production: What to Track and Why It’s Different

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Learn how to monitor AI agents in production by focusing on conversation-level signals, multi-step trajectories, and real user interactions rather than traditional system metrics. The article explains why agent observability differs from standard APM due to infinite input space and non-deterministic LLM behavior, and highlights the need to capture prompt-response pairs, multi-turn context, and tool usage traces. It also outlines how production traces become the foundation for continuous improvement and scalable evaluation, combining automated evals with selective human review to maintain quality at scale.

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