The AI failure mode I keep seeing in production that nobody talks about enough

Not hallucinations — that's expected now and everyone's built around it. I mean something different: the model's output is internally sound, but its understanding of the *situation before it acted* was wrong.

The pattern I keep running into: an agent or pipeline makes a consequential decision, every unit test passes, the logic traces back correctly — but the premise it was operating on was stale or subtly off at the moment it mattered. The output was consistent with its world model. Its world model just didn't match reality.

What makes this hard to catch: humans do this verification implicitly. You glance at a situation before acting and something feels off, so you pause. That reflex doesn't exist in most deployed systems. You end up with perfect audit logs of what the model did, but no visibility into why it thought the world looked like X at that moment.

I've been thinking about this a lot and curious whether others have hit it. Specifically: has anyone actually built upstream verification into production systems — something that checks whether the model's situational understanding is grounded before it acts — rather than catching the failure in post-hoc logs?