Imagine a customer support chatbot that confidently tells a user they have 365 days to return a product. The actual policy is 30 days. Every system metric is green: P95 latency at 142ms, throughput at 1.2k requests per second, eval accuracy at 94.2%, error rate at 0.02%. The dashboard is a wall of healthy green badges. The answer is still wrong. And nobody knows until a customer tries to return something eleven months later. This scenario is made up. The pattern is not. Variations of it are happening right now in production AI deployments across every industry, and the industry has almost no tooling to catch it. The Failure That Doesn't Page You Traditional software fails visibly. A service throws a 500. A database connection times out. A container crashes. You get paged. You look at the stack trace. You fix the bug. AI pipeline failures don't work like that. The system keeps running. The latency stays low. The throughput stays high. The error rate stays near zero.…