Key takeaways
Spend human attention where the risk is highest.
Put everything a reviewer needs on one screen.
Every correction should become a test.
Human review is the reason most teams feel safe deploying AI agents. It is also the first thing to break when volume grows. The answer is not less review. It is better placed review.
Review by risk, not by habit
Sort actions by what happens if they go wrong. Drafting a reply is low risk. Issuing a large refund is not. Let low risk work run on its own and reserve people for the decisions that carry real consequences.
Make every approval fast
A reviewer should see the request, the sources and the proposed action on one screen. If they have to open three tools to check an agent, the agent has not saved any time.
Turn decisions into training data
Every approval and correction is a signal. Feed those decisions back into evaluations so the same mistake does not reach a person twice. Over time, review shrinks because the agent improves, not because anyone stopped checking.
Define the limits in plain language
Review rules fail when nobody can explain them. Write them the way you would brief a new colleague: refunds under a set value are fine, anything touching a contract needs a second pair of eyes, and any mention of legal action goes straight to a person.
Short rules are easier to test and easier to change. They also give reviewers a clear answer when they ask why a case landed in their queue.
Watch the queue like a product
A review queue is a product with its own users, and it can fail the same way. If reviewers approve almost everything, the rule is too strict. If approvals take hours, the agent is not saving time, it is only moving the wait.
Approval rate by rule, to find checks that add nothing
Time to decision, to spot slow steps
Overturn rate, to see where the agent is wrong
Queue age, to catch work that is going stale
Protect the reviewers from fatigue
Approving a hundred good actions in a row trains people to stop reading. By the time a bad one arrives, the reviewer is on autopilot. Good systems fight this on purpose, by keeping queues short and by mixing in known test cases to check that attention is holding.
Rotate reviewers, cap session length and show people the effect of their decisions. When a reviewer can see that a correction stopped the same error next week, the work feels less like checking boxes and more like teaching.
Let review shrink over time
The goal of review is to need less of it. When a type of task has been approved without changes for long enough, lower the sampling rate and keep a small random check in place. If quality dips, the rate goes back up on its own.
Done well, people stop being a bottleneck and become the coaches who decide what the agent should learn next. That is better work for them and a safer system for everyone.
About the author
Ines Okafor
Ines leads customer engineering at Falak and has helped teams in finance, insurance and logistics put agents in front of real work.
