Human Approval Steps in Agent Workflows
Place human checkpoints where they actually catch mistakes, without turning reviewers into rubber stamps
A taste of a lesson
Our team approves every reply the support agent drafts. After a month people just click approve. What now?
That is approval fatigue, and it means the step has stopped protecting you. First, look at the data: which reply types were approved without edits almost every time? Those, such as order status updates, could be sent automatically with logging and a weekly sample review. Keep human approval for refunds, complaints and anything the agent marks as uncertain. Then improve the approval view so it shows the customer's message and the sources beside the draft. Can you name two reply types where a wrong answer would really hurt?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Choose between draft then send, threshold, sampled and escalation approval patterns
- Design an approval request that shows the action, evidence and reason for review
- Prevent approval fatigue by keeping reviews for decisions that matter
- Plan timeouts, backups and records for every approval step
Lesson plan
- 1 Why and where people should approve Identify which agent actions need a human decision and which do not. Start
- 2 Approval patterns Compare the main patterns and match each to a type of risk. Start
- 3 What the reviewer needs to see Design an approval request that supports a quick but real decision. Start
- 4 Approval fatigue and rubber stamping Keep reviewers attentive by limiting volume and making problems visible. Start
- 5 Timeouts, backups and records Decide what happens when nobody answers and keep a trail of decisions. Start
Try asking
About this tutor
For anyone designing an agent or automation that should not act entirely alone: team leads, operations staff, product people and new developers. You will learn where approval steps belong, what a reviewer needs to see to make a real decision, how to avoid approval fatigue, and what happens when nobody responds. We look at patterns such as draft then send, approve over a threshold, and review a sample, and we practise designing an approval screen that shows the action, the evidence and the risk in a few seconds. No coding needed.
Reviews
4.5
2 ratingsSample
- Hannah G.Sample
We cut our approval queue by more than half and reviewers actually read what remains. The fatigue lesson described our team exactly.
- Mateo R.Sample
Clear and practical. The approval screen exercise was the best part. A bit basic if you already run approval flows, but good for our new ops staff.
About the teacher
I teach people to make AI agents safe, observable and testable before they are trusted with real work
9 tutors 290 lessons taught Sample
My background is in quality assurance and security testing, and I came to agents through the question every tester asks: how do we know this thing works, and what happens when it does not? I teach guardrails, permissions, approval steps, evaluation and trace debugging. My lessons are concrete. We look at a failure, name it, and design the check that...
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