In most contact centres with an AI agent, there is a second, invisible job being done. When the AI gets something wrong, a human agent catches it and quietly puts it right: corrects a wrong answer before it reaches the customer, redoes a botched escalation, cleans up after a loop. The agents do this all day, and almost nobody is counting it.
This article is about that hidden correction work: what it costs, why the AI never learns from it, and what to build so it stops being invisible.
The correction work nobody logged
The hidden work takes two forms. Sometimes the agent intercepts an AI mistake before the customer sees it. Sometimes the customer sees it, escalates, and the agent fixes both the original problem and the damage. Either way the agent has done extra work, caused by the AI, that the system records as a normal contact.
So the cost is real and it is paid in agent time, but it does not appear anywhere as "cost of AI errors." It is absorbed into general handle time, which makes it look like the agents are simply a bit slower, rather than busy cleaning up after a tool.
Why the AI never gets better
The more serious cost is that the AI does not improve. An agent who corrects an AI mistake has just produced exactly the information that would make the AI better: here is what it got wrong, here is the right answer. That information is worth a lot. In most contact centres it is worth nothing, because there is nowhere for it to go.
The agent fixes the contact and moves to the next one. The correction is not captured, not routed to whoever tunes the AI, not turned into a change. So the AI makes the same mistake tomorrow, an agent corrects it again, and the loop runs forever with no learning in it. The missing piece is not better AI. It is the channel that carries a correction from the agent back to the system.
Why agents do not report it themselves
The obvious question is why agents do not just flag the AI's mistakes. The answer is that the incentives quietly tell them not to. Kevin Davis, who writes KD Be Schemin, has made the point that staff resistance around AI is often rational rather than cultural: people respond to how they are measured.
An agent measured on contacts closed and handle time has no reason to stop and document an AI failure. Documenting it is unpaid extra work that makes their own numbers look worse. The fastest path, the one the metrics reward, is to fix the contact silently and move on. Mark Levy, in Decoding Customer Experience, frames this as part of what has to be fixed before AI can deliver: the surrounding system, not the model. The agents are behaving sensibly. The system never asked them to do anything else.
Make the correction loop real
An AI deployment without a correction loop has a structural flaw: its mistakes are absorbed by people instead of being fixed in the system. The agents keep it looking like it works, at a cost paid in their time, and the AI stays exactly as good as the day it launched.
Closing that loop is a specific, buildable piece of work: a way to capture corrections, an incentive that makes capturing them worthwhile, and a route from the agent back to the people who can change the AI. Until that exists, the frontline is doing a job the company has not acknowledged it created.