How to Debug & Evaluate AI Agents with Observability — LangChain Guide
Agent observability is crucial for evaluating AI agents due to their non-deterministic behavior, which traditional software observability cannot capture. Evaluations differ from software testing by focusing on reasoning, context, and state evolution across multi-turn interactions. Traces provide data for assessing agent behavior at various granularities, such as single steps or full conversations, and are essential for diagnosing issues in real-world scenarios. The evaluation process requires selecting appropriate granularity levels, with full-turn evaluations relying on detailed trace data and single-step evaluations using run captures. Ensuring trace data captures all necessary context is critical for accurate assessment of agent performance.
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