An artificial intelligence execution trace captures what happened during a model or agent run. It can record decisions, tool requests, responses, timestamps, errors, retries, and transitions between workflow stages.
Tracing helps teams diagnose failures, evaluate behavior, compare runs, and understand how an outcome was produced. Sensitive data should be minimized or protected rather than copied indiscriminately into the trace.
