An artificial intelligence execution trace preserves what happened during a production session rather than only the final answer. It can include intermediate model responses, tool calls, errors, timing and observations needed to reconstruct the workflow.
Traces provide realistic material for evaluation and debugging. They may contain private data, so platforms should sample, redact and retain them under explicit policies while preserving enough provenance for reviewers to understand each case.
