2026-07-15
Why Most Agent Loops Break in Production — It's Usually Not the Model's Fault
Production failures in agent systems rarely come from the LLM making a bad call. They come from unwritten assumptions about tool reliability, state, and error handling.
← Back to BlogWhen an agent loop fails in production, the instinct is to blame the model. It hallucinated. It picked the wrong tool. It got confused. But if you dig into enough production incidents, a pattern emerges: the model was set up to fail by assumptions that nobody wrote down. The tool that returns a 200 OK with an error message in the body. The state that looks valid but is actually from a stale run. The retry logic that kicks in but doesn't back off, hammering a downstream service that's already degraded. The model is the easiest thing to blame, but it's almost never the root cause. Production-grade loops need defensive parsing of tool outputs even when the HTTP status looks fine, explicit state freshness checks before making decisions, exponential backoff with jitter on every external call, and a hard timeout per loop iteration plus a total budget. None of these are clever. They are boring, and boring is what keeps things running at 2 a.m.
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