AI agent observability makes it possible to understand why a multi-step run succeeded, failed or took too long. Useful traces show the sequence of model calls and tools, the state passed between steps, retry behavior, elapsed time and token usage. Sensitive values should be redacted before logs are stored.
A useful trace for one run lists each model call with its prompt size and latency, each tool call with arguments and result, and the final outcome. When a user reports a wrong answer, you can open that run and see whether a tool returned bad data, the model ignored a result or a stop rule fired too early. Traces often contain customer data, so set retention and access rules. Good traces also show where error handling and retry logic kicked in.
Without traces, debugging an agent often means guessing from its final response. Add structured logs and alerts, then use recurring failure patterns to improve prompts, tool schemas and stopping rules. Related: AI Agent, Inference Latency.