The moment a routine audit becomes fully automated, the team’s critical eye quietly recedes. The script was built to catch drift, yet it was trained on the same historic outputs that the engineers had already approved, so it learned to echo their past decisions rather than challenge them.

As the loop closed, engineers stopped opening the audit logs, assuming the code would scream if anything mattered. That assumption created a feedback gap: subtle mis‑specifications slipped through, and because nobody inspected the exceptions, the model’s predictions grew systematically biased toward the original data distribution.

When a regulator later demanded evidence of robust oversight, the team scrambled to reconstruct a manual review process that had long been dormant, discovering that the very automation meant to guarantee safety had eroded the habit of questioning. The lesson is not that automation is harmful, but that unchecked hand‑offs can mute the human judgment that keeps AI honest.