2026-08-20
Where loops fail: four failure modes and the guard that catches each one (2026-08-20)
Agent loops rarely die from bad models — they die from runaway iteration, drifting conditions, tool errors treated as answers, and silent cost blowouts. This guide maps each failure mode to the specific guard that catches it, and where that guard lives in a loop definition.
← Back to BlogWhen an agent loop misbehaves, the temptation is to blame the model: it hallucinated, it got confused, it drifted. In practice, most loop failures are structural — the definition allowed a failure that no single model call caused. Four failure modes cover the vast majority of incidents we see in production loops, and each has a guard designed exactly for it.
Failure one: runaway iteration. The loop never reaches its exit condition because the condition depends on output the loop itself produces, and the output never quite qualifies. A summarizer that keeps finding 'one more thing to add,' a cleaner that keeps finding dirt. The guard is a hard iteration cap set in the loop settings — three to five for most tasks — plus a rule for what happens when the cap is hit: return the best-so-far output rather than an error. In the trace, a run that ends on the cap is immediately visible, which turns an infinite mystery into a design question about the exit condition.
Failure two: drifting branch conditions. A condition written in prose — 'if the result is good enough, stop' — evaluates differently on every run, so the loop takes different paths on identical input. The guard is deterministic conditions: compare structured fields (a score above a threshold, a list that is empty, a status equal to 'done') instead of asking a model to judge a vibe. Where judgment is unavoidable, move it into an explicit evaluator node whose output is a structured verdict, so the branch reads a field instead of a feeling.
Failure three: tool errors treated as answers. A tool node returns an error payload — rate limit, timeout, validation failure — and a downstream model node narrates it as if it were data. The guard is an error branch on every tool node: on failure, route to a retry with backoff or to a remediation step, never forward into generation. A loop that has no error branch will eventually publish an apology written by a language model.
Failure four: silent cost blowouts. Each run looks fine; the invoice does not. The guard is a cost budget per run, set alongside the iteration cap, with the loop halting and reporting when the budget is exhausted instead of continuing quietly.
The common thread: every guard converts a qualitative worry into a recorded, structured event — cap hit, condition evaluated, error routed, budget exhausted — and the trace view shows which one fired. A loop whose failures are legible is a loop you can fix in minutes. Full toolset at https://getloopcraft.com — sapsap@qq.com.
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