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Training Your Team to Reject Agent Output Well
Rejecting AI output is a skill. What to check first, how to record the reason so the system improves, and how to make saying no cost nothing socially.
Written by Sicherhaven
Your approval step is in place and almost nothing gets rejected. That is not a sign the agent is excellent. It usually means people do not know what to look for, or saying no costs more than saying yes.
Rejection is a skill and it can be taught. Three parts make it work: a short ordered list of what to check first, a way to record why something was rejected, and a culture where returning an item is unremarkable. Miss any one and the approval step degrades into a click.
Teach an order, not a checklist
Long checklists get skimmed. What people need is an order of operations, so the highest yield check happens while attention is fresh.
Teach this sequence.
- Who receives it. Recipients, audience, visibility. A correct message to the wrong person is the most common expensive error and the fastest to check.
- Numbers and dates. Anything quantitative, against a source the reviewer can actually see. If they cannot see a source, that is a design problem to raise.
- Claims about the world. Statements presented as fact that the reviewer can verify from what they know. Confident phrasing is not evidence.
- Tone and framing. Does this commit to something, apologise for something, or imply a decision that has not been made?
- Everything else. Formatting, wording, style. Last, because it is where people naturally start and where the least damage lives.
Most reviewers, left alone, begin at the bottom of that list. Grammar is easy to see and consequences are not.
Make the reason a small number of options
If rejecting requires writing a paragraph, people will approve marginal items to avoid the effort. That is a design failure dressed up as a judgement call.
Give four or five preset reasons and a free text box that is optional. Something like: wrong recipient, factual error, missing information, wrong tone, needs a human to write this. Anything more granular and people pick at random.
The point of the preset is not precision. It is that the reason gets recorded at all, which turns a scattered set of individual corrections into a pattern you can see.
Read the rejection data monthly
Rejections are the most useful signal you will get about an automated workflow, and most organisations throw them away.
Look at the mix. Mostly "missing information" means the agent is not getting the context it needs, and the fix is upstream. Mostly "wrong tone" means expectations were never written down. Mostly "needs a human" means the workflow was scoped too broadly and part of it should come back out of automation. Summaries of conversations are a common thing to pull back, since automated meeting notes change what people say in the room.
Look at the rate too. A rejection rate near zero is worth investigating as seriously as one near half. Near zero usually means nobody is really looking.
When agent work sits on the same set of records as the projects and people it concerns, as it does in SicherOne, this data is attached to the workflow rather than scattered across tools, which makes the monthly read a ten minute job instead of a project.
Make saying no socially cheap
This is the part that decides whether any of it works, and it starts with how you went about telling staff an agent is doing part of their job.
If the person who set up the workflow reacts defensively to rejections, rejections stop. If a manager describes a high rejection rate as a failure of adoption, rejections stop. If returning an item means a conversation, rejections stop.
Three practical moves. Say explicitly at launch that a return requires no explanation beyond the preset reason. Have the process owner thank people for returns in the first weeks, publicly and without irony. And never report rejection rate as a performance metric for reviewers, because the moment it is one, the number becomes meaningless.
Practise on deliberately bad output
The most effective training we have seen is also the simplest. Take real outputs, introduce errors into some of them, and have the team review a mixed batch.
Wrong recipient in one. A plausible but wrong figure in another. A tone that commits the company to something. Leave several untouched.
People find out quickly which errors they catch and which slide past. Almost everyone misses the plausible wrong figure on the first attempt, and almost everyone catches it afterwards. Twenty minutes of this does more than an hour of explanation. It is also the quickest way through the first week when you are onboarding a new hire alongside an agent that already knows the job.
Give reviewers permission to escalate
Some items should not be approved or rejected. They should go to someone else.
An output that is fine but raises a question about whether the workflow should exist, or one that touches something the reviewer does not feel qualified to judge, needs a third option. Without it, reviewers guess, and guessing usually resolves as approval.
Name the escalation path and the person on the other end. A reviewer who knows where to send something they are unsure about will send it. One who does not will click approve and hope.
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