In AI agent work, a correction is any change a reviewer makes to an agent's proposed action before it takes effect: editing a draft, changing an amount, choosing a different route, or declining the action with a reason. It is the everyday act of human review, and each one is filed in the record alongside the agent's original proposal.
Why do corrections matter?
They are where oversight becomes visible. An approval with no edits could mean the agent was right or that the reviewer did not look. A correction proves that a person read the work and changed it, and the record shows exactly what changed.
Corrections are also the main signal about the agent's quality. If reviewers keep making the same edit, the agent has a gap. If corrections suddenly rise after a policy update, its sources may be out of date. If they fall to almost nothing, either the agent has become very good or reviewers have started waving work through. A contact centre manager reads those patterns daily, a skill described in what contact centre managers should learn before working with AI agents.
What makes a good correction?
Three things.
- It fixes the case. The customer, supplier or colleague receives the right outcome.
- It carries a reason. One sentence on what was wrong, written so someone else can understand it without opening the case.
- The reason generalises. "Used the superseded fee schedule" helps the agent owner; "fixed it" does not.
The reason is what turns a correction from a private fix into evidence. An auditor reviewing oversight will look for exactly this, as covered in what internal auditors should learn before working with AI agents.
An example
Take a hypothetical utility's billing team. The agent drafts a reply to a customer disputing an estimated bill and proposes a payment plan. The reviewer sees that the customer submitted a meter reading two days earlier, which the agent did not use. She changes the draft to rebill on the actual reading, removes the payment plan, and records the reason: "agent ignored a customer reading submitted after the estimate date". The customer gets the right bill. The reason travels to the team lead.
How is a correction different from a banked correction?
A correction is local: it fixes one case and stops there. A banked correction is the lasting change that follows when the reason behind corrections is turned into a change to the agent's brief, sources, bounds or certification. In the billing example, banking would mean the agent owner adds a rule to check for recent customer readings before replying.
A correction is also different from a decline. Declining rejects the proposed action entirely; a correction may keep most of it. Declines need an especially clear decline reason, because the agent does not get a second attempt on that case.
Who should make corrections?
Only reviewers with current standing for that kind of action. As agents take on routine work, teams often shift people from doing the work to correcting it, and the change in skills is real. The guide on how to reorganise a team around AI agents without losing control covers that move, and the guide to AI agent skills by role maps who needs which skills.
Where the Academy fits
Pryme Intelligence Academy treats correcting well as a core reviewer skill. The Operator track's modules on human review and teaching the agent cover writing reasons that generalise, and the practicum assesses two weeks of real review decisions, signed off by a Pryme assessor.
Questions people ask
Should reviewers correct small wording issues?
Only where it matters to the outcome or the customer. Correcting taste rather than substance adds noise to the record and makes real patterns harder to see.
Does the agent see the correction?
Not in the moment. The correction is filed and the outcome goes ahead. The agent changes only when someone banks the reason as a deliberate change.