How a 0.2% AI Hallucination Rate Caused 245 Retries in a 5-Day Loop

A team running around 100 unattended LLM agents discovered that one pipeline task had retried 245 times over five days before they caught it. The root cause was a conflict between two automated agents: a reviewer hallucinated formatting requirements that did not exist in the original document, and its fix instructions were mathematically incompatible with the producer's minimum output contract. Because no single response could satisfy both constraints simultaneously, the producer kept failing and retrying indefinitely. An audit of 2,038 stored reviews found a 0.2% hallucination rate, which was enough to trigger nearly 500 wasted processing cycles. The team has since patched the pipeline by passing the original request into review prompts, flagging impossible instructions before they enter the queue, and capping consecutive failures at five before routing the task to a human reviewer.