Rubber-stamping is a volume problem, not an attitude problem
When every agent action routes to the same approval queue, reviewers face volume that makes careful reading impossible, so approval collapses into pattern-matching on 'does this look like the usual case.' That's not a training issue; it's an inevitable consequence of the queue design.
The fix is tiering: route only genuinely ambiguous or high-impact actions to a human, and let the deterministic controller handle everything within a clearly defined, low-risk envelope without a human in the loop at all.
Give reviewers the context, not just the decision
A good approval screen shows why the agent proposed this action, what evidence it retrieved, and what the alternative options were — not just a yes/no on the final output. Reviewers approve faster and more accurately when they can see the reasoning trail, not just the conclusion.
This is also what makes the approval meaningful from an audit perspective: a decision made with visible reasoning is defensible later; a bare yes/no is not.
Measure the queue, not just the approvals
Track approval latency and rubber-stamp rate (approvals given in under a defined, implausibly short time) as first-class operational metrics. A rising rubber-stamp rate is an early warning that the checkpoint has stopped doing its job, well before an actual incident proves it.
Treat that metric the same way you'd treat a rising error rate on any other system component — because that is exactly what it is.
An approval gate that is always clicked through is not oversight. It is latency with no safety benefit.MNB AI Practice
Key actions
- Tier actions by risk; route only ambiguous/high-impact ones to humans
- Show the reasoning trail, not just the final decision
- Track rubber-stamp rate as an operational health metric
- Automate the clearly low-risk envelope entirely