Human-in-the-loop

Agents make mistakes that look convincing. A hallucination reads just as fluently as a correct answer. Without a check, a wrong price, a wrong claim or a wrong deletion goes straight to a customer or into the books.

It also lets trust grow in steps. A team watches where the agent proves reliable, relaxes the check there, and keeps it where a mistake would cost real money or reputation.

• List the steps in a workflow and mark the few that cannot be undone or carry real cost: sending a message, paying an invoice, deleting data, quoting a price.
• Let the agent run the rest at full speed.
• Put the review just before the consequential action, not at the start, where it adds delay without adding safety.
• Show the reviewer what they need to decide: the proposed action, the source it used and a one-click approve or edit.
• Track how often the reviewer changes something. A check that has not changed anything in months is a candidate to loosen.

Human-in-the-loop is a design choice for automation. An AI agent does the preparation, such as drafting a reply, proposing a refund or filling in a record, and a person checks it before anything irreversible happens.

It sits between two extremes. At one end a person does everything by hand. At the other, an autonomous agent acts alone. Human-in-the-loop keeps the speed of the agent and the judgement of the person at the points where judgement counts.

• Asking a person to approve everything. They become the bottleneck the agent was meant to remove, and they start approving without reading.
• Giving the reviewer no context, so the check is a formality.