What is Agent trajectory logging?
Also called trajectory capture, agent run history, reasoning trace logging.
Agent trajectory logging is the practice of recording the full ordered sequence of steps an autonomous agent took during a run, including the messages it received, the intermediate summaries it produced, the tools it called with their arguments and results, and the final outcome. The stored trajectory lets a run be replayed, audited, and evaluated after the fact.
A trajectory record stores the ordered events of a run: the incoming request, the context assembled for the model, each model response, each tool invocation with its arguments and result, any approval or interruption, and the final output. Records are keyed by a run identifier so they can be correlated with traces, cost data, and the resulting user outcome.
This matters because an autonomous run chooses its own path. When the result is wrong, the interesting question is which step went wrong and why, and that is rarely visible from the final answer. Trajectories are also the raw material for evaluation sets, for regression tests built from real failures, and for audits where someone must show exactly what an automated system did on a given date.
Three mistakes recur. Trajectories often contain customer data, so storing them without redaction or a retention limit creates a privacy liability. Volume grows quickly, so unbounded capture becomes an operating cost of its own. And any text a model produces describing its own reasoning is generated output, not an instrument reading, so it should be treated as a useful hint rather than a faithful record of internal computation.
Trajectory logging overlaps with tracing and is often implemented on top of it, with each step recorded as a span carrying the semantic detail. The difference is emphasis. Tracing optimizes for timing and dependency structure, trajectories for decisions and content. Evaluation harnesses and replay tooling consume trajectories, and cost per action is normally computed by summing the usage recorded across one trajectory.
Key points
- Records every step of a run, not only the final output.
- Enables replay, auditing, and evaluation after a run completes.
- Stated reasoning text is not a guaranteed account of internal computation.
- Requires redaction and retention limits because payloads contain personal data.
In practice
A scheduling agent books the wrong meeting room. The trajectory shows it searched availability, received two candidate rooms, called the booking tool with the second one, and got a success response. The error was not in the booking step but in the search filter, which omitted capacity. Without the stored trajectory the team would only see a confirmed booking and a confused user.