u/Bright_Inside7949

I’ve been looking at a recurring failure pattern across AI systems in production. Not model failure, or data quality or infrastructure.

Something else. Where system continues to operate exactly as designed, models run, outputs look valid, pipelines execute and governance signs off

But the underlying assumptions have shifted. So you end up with decisions that are technically correct, but contextually wrong. Most organisations respond by tightening controls, reducing overrides or increasing monitoring.

Which just reinforces the same behaviour. I’ve tried to map this as what I’m calling the “Formalisation Trap”, where meaning gets locked into structure and continues to be enforced even after it stops reflecting reality.

Has anybody else seen similar patterns in production systems?

Asking given what was announced …

If AI agents start running workflows directly (no UI, no human in the loop), who actually owns the decision logic over time?

Not who built it, who updates it when “good” changes?

Feels like most orgs don’t have an answer to that yet.

They happen when a human overrides the system, and there’s no structured way to capture or explain why.

Over time, you end up with two systems, namely what the model says and what the organisation actually does

What are you observing on how people are handling that boundary?

Are you capturing overrides as data, or are they still invisible?