2026-08-17
Reading your agent loop's trace: a systematic way to debug without guessing (2026-08-17)
When a loop misbehaves, the trace is not a log to skim — it is a recording of every decision. This guide shows how to bisect a trace, isolate the first divergent step, and tell whether the fault lives in a prompt, a condition, or a tool response.
← Back to BlogMost teams debug agent loops the way they debug feelings: re-run the loop, stare at the final output, and guess. That approach fails because an agent loop is a chain of decisions, and a bad final answer is usually several steps downstream from the step that actually went wrong. The trace records every one of those steps. Learning to read it systematically turns a fuzzy 'it feels broken' into a precise 'node X produced the wrong branch at step 7'.
Start by bisecting, not by reading top to bottom. Find the midpoint of the trace and check the loop state there. If the state is already wrong, move earlier; if it is still correct, move later. Each bisection halves the search space, and within a handful of checks you have isolated the first divergent step. This is the single highest-leverage habit in loop debugging.
Once you have the first bad step, classify it into one of three homes. If the step is a model invocation and the output is wrong, the fault is in the prompt or the context fed to it — inspect exactly what the model saw, not what you intended it to see. If the step is a condition node, the fault is in the branching logic — a condition evaluated on stale data, an inverted comparison, or a threshold that never fires. If the step is a tool node, the fault is in the response — a schema the model misread, a timeout, or a stub that leaked into production.
The three homes need different fixes, which is why classification matters. A prompt fault is fixed by tightening instructions or adding examples. A condition fault is fixed by moving or rewording the branch. A tool fault is fixed upstream, in the tool itself or its error handling. Applying a prompt fix to a condition bug changes nothing and costs you another round of guessing.
Two trace signals repay extra attention. First, the transition between steps: the data a node emits is rarely the data the next node expects verbatim, and most bugs hide in that mapping. Second, the loop's exit: record why the loop stopped — budget exhausted, max iterations hit, or a genuine success condition. A loop that always exits on max iterations is not finishing its work; it is timing out quietly.
Finally, make debugging reproducible. Pin the inputs that produced the failure so you can replay the exact run, and use dry-run simulation with stubbed tool responses to step through the branching logic without spending real calls. LoopCraft's trace view supports step-by-step playback and per-branch cost estimates, so you can reproduce, isolate, and fix in one session instead of ten. Full trace tooling at https://getloopcraft.com — sapsap@qq.com.
Reading time: 6 min