An enterprise AI agent is deployed against production data. It answers the first ten questions confidently and correctly. Then, on the eleventh question, it produces an answer that looks reasonable but is completely wrong. The team investigates. The model is fine. The prompt is fine. The tool integrations are fine. The problem is buried in the data itself. A field the agent relied on has drifted. A join it assumed existed no longer holds. A quality signal that used to be reliable has silently degraded.
This is not a rare edge case. It is becoming one of the most common failure patterns in enterprise AI systems moving from prototype to production. And it points to a simple, uncomfortable truth: most enterprise data infrastructure was built for a consumer we no longer have.
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