1. Orient in the summary
Start with outcome, diagnosis, cost, timing, and the highest-impact issues instead of scanning an unstructured log.
AI agent debugging workflow
Find the root cause of an AI agent failure faster.
TraceProbe turns a noisy agent run into a readable execution story: what happened, where it slowed down, which tool or step failed, and what to investigate next.
Move from raw logs to a clear diagnosis.
When an agent fails, the hardest part is often reconstructing the sequence of events. TraceProbe brings the run summary, timing, reasoning, and tool activity into one focused view for local analysis on Windows.
- See the failed step and surrounding execution context.
- Separate slow tool calls from reasoning or configuration problems.
- Review recommendations without sending trace content to an external service.
A repeatable debugging loop
Use the same workflow for a single failed run or a recurring production regression.
2. Inspect the timeline
Follow the run phase by phase to locate latency spikes, failed tool calls, retries, and unexpected branches.
3. Decide what to change
Use the execution context and recommendations to form a concrete next experiment, then compare the next run.
Debug the execution, not just the final answer.
A final response can look plausible even when an agent took a bad path. The timeline helps you see retries, tool latency, incomplete work, and the exact point where behavior changed.
Try the debugging workflow on Windows.
Install TraceProbe, open a sample run, and start the in-app trial.