AI Agent Failures Are Distributed Systems Failures. Here's the Complete Mapping.

A few months into building an AI agent pipeline for a fintech client, we had a silent failure that cost us three days. The agent processed a document. Returned a confident-looking response. No error, no exception, no log entry that suggested anything was wrong. That output went into the next step, which used it to write a decision record. The decision record went downstream. Three steps later, a human reviewer flagged something that did not add up. The root cause was a hallucinated intermediate field. One field. The model had made up a plausible-sounding value for something it should have extracted from the document. Everything downstream had treated that invented value as real. I had seen this failure mode before. Not in AI. In distributed systems. The microservice that returns 200 OK while writing corrupted data. The queue consumer that marks a message processed before finishing the work. The retry that fires twice because the ACK never arrived. Same pattern. Different worker. The me