Evaluation and Observability
Trace Analysis
A trace is the full recorded sequence of an agent's decisions, tool calls, inputs, outputs, and intermediate results across a single task execution, giving you a complete audit trail of every step the agent took. Without traces, agent failures are opaque: you see a wrong answer but cannot determine whether reasoning went wrong, a tool returned bad data, or the agent misread its context; traces make non-deterministic failures understandable and fixable. In production, traces also surface performance problems such as which tool calls are slow, which reasoning steps waste tokens, and where the agent loops unnecessarily.
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LangSmithsmith.langchain.comLangChain's observability platform for tracing, evaluating, and debugging LLM applications (smith.langchain.com)Braintrustbraintrust.devEnd-to-end platform for logging, evaluating, and iterating on AI agent traces (braintrust.dev)Arize Phoenixphoenix.arize.comOpen-source observability for LLM applications with trace visualization (phoenix.arize.com)OpenTelemetry for LLMsopentelemetry.ioAdapting the OpenTelemetry standard for distributed tracing to LLM agent systems (opentelemetry.io)