Why Agents Get Stuck in Infinite Loops
Infinite loops in the lab are easy to catch — just glance at the console. But failures in production are far more subtle: users won't say "your Agent is looping." They'll say "why is your AI so slow" or "is it broken?"
The model forgot it already did this, or thinks it didn't succeed and needs to retry — but with identical parameters, the result is always the same.
Typical scenarios: reading the same file repeatedly, searching the same keyword over and over, calling the same API in a loop
The Agent is busy, but each iteration only does marginal work: reformatting, re-confirming, irrelevant searches. It looks busy but makes no real progress toward the core goal.
Typical scenarios: Agent loses direction on a complex task, keeps making safe but useless micro-moves
When the context is too long or the model is confused, it degrades into repetition mode: regenerating previous output verbatim. The model isn't frozen — it's lost.
Typical scenarios: late in a long conversation, context nearing the window limit, vague task description
Tool A times out → Agent retries → still times out → tries a different call → still fails → falls back to Tool B → B depends on A's result and also fails → cascade.
Typical scenarios: external API rate limiting, database connection pool exhausted, third-party service temporarily down