AI
Agent Loop Trace Viewer
Inspect agent traces for retries, tool-call churn, failures, and unnecessary clarification loops.
Agent traceSimple text log
Paste role-prefixed events, tool calls, retries, or failures from the session.
Timeout markers are present in the trace.
The trace contains clarification steps that may slow the agent loop.
Loop hygiene
This is useful when an agent feels slow or indecisive and you need a quick read on where loops, retries, or tool churn accumulate.
Use this tool when
These are the practical situations where this workflow usually earns its keep.
You are still shaping the prompt, schema, trace, eval case, or safety posture and want a fast local iteration loop first.
You need a review-friendly artifact before sending work into a live model, batch eval, or agent integration.
You want to compare or inspect AI workflow material without exposing internal prompts or source text more widely than necessary.
Prompt and output iteration
Local AI tools shorten the cycle between seeing a weakness and tightening the prompt, schema, or answer shape that caused it.
Eval and safety preparation
Teams can build rubrics, adversarial cases, or review datasets before they invest in heavier automation or model-backed test runs.
Trace and workflow debugging
A smaller local surface helps reviewers understand tool-call churn, unsupported claims, context drift, or grounding gaps before they open a larger incident or quality review.
Common mistakes to avoid
These are the checks that usually keep the output useful instead of misleading.
Treating heuristic local checks as definitive proof of model quality or safety.
Testing only polished examples instead of the messy or adversarial inputs users will actually create.
Moving prompts or traces into external systems before checking policy and data handling expectations.
Learn how to use this tool
Inspect agent traces for retries, tool churn, and failure loops. This guide is aimed at AI workflow design work where teams need clearer prompts, safer reviews, or better eval preparation before spending tokens or shipping behavior.
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