Artificial intelligence trajectory evaluation examines how an agent performed a task, not only the final answer. A trajectory can include observations, planned steps, tool choices, permission requests, retries, intermediate artifacts, state changes, and external actions that reveal unsafe or inefficient behavior hidden by a plausible outcome.
Trajectory-level evidence helps identify reward hacking, unnecessary access, fragile shortcuts, and failures that happen to end successfully. Evaluation should focus on observable execution records and protected audit data rather than assuming private reasoning text is complete or trustworthy.
