An AI 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.
ELI5
An execution trace is a step-by-step record of what an AI workflow did. It can show which tools were called, what results came back, where an error occurred, and how the system reached its final outcome.
For example, if a research agent produces an incomplete report, the trace may show that one source failed to load and the agent continued anyway. That evidence helps an operator diagnose the real failure instead of guessing.
Why trace AI agent workflows?
Tracing supplies evidence for debugging, monitoring, evaluation, accountability, and recovery when a workflow behaves unexpectedly.
What should an AI execution trace contain?
It should contain bounded task state, relevant inputs, tool activity, outputs, errors, timing, retries, and identifiers needed to reconstruct the run.
