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Recording the full step-by-step path of a request through your agent so you can see exactly what happened.
Instead of a single log line, a trace stitches together every LLM call, tool invocation, retrieval, and retry into one tree you can replay — essential because agentic failures hide in the handoffs between steps, not in any single call. With it, 'the agent gave a wrong answer' becomes 'step 4 retrieved the wrong doc, so the model reasoned correctly over bad data.' Tools like LangSmith, Langfuse, or OpenTelemetry-based stacks capture these.
Plainly
Think of Tracing as roads and power for the app city. Recording the full step-by-step path of a request through your agent so you can see exactly what happened.
In practice
Use it when local behavior needs to become a reachable, reliable deployed service. In practice, define the owner, input, output, and failure mode before you rely on it.