Human-in-the-loop: design for the handoff, not the override

Most human-in-the-loop systems are built as if the human is a backstop — present to override the model when the model is wrong. That framing produces interfaces where the human is asked to review long, dense agent traces and approve or reject. Approval rates are high, error catch rates are low, and within three weeks the humans rubber-stamp by default.

What goes wrong with the override pattern

Reviewing an agent’s plan after the fact is harder than producing one from scratch. The reviewer has to reconstruct context the agent had, follow chain-of-thought they didn’t write, and spot the one wrong step in twenty correct ones. This is hard for humans, and it gets harder as the agent gets more capable, because the cases that need review are exactly the cases where the agent’s reasoning is sophisticated enough to look correct.

What handoff-first design looks like

The agent does the work where its judgment is reliable, then surfaces the specific decisions where human judgment is needed — and only those decisions, with the relevant context, formatted for fast review. The human isn’t reviewing a transcript; they’re answering a question. The threshold for “needs human” should adjust based on uncertainty signals from the model itself, not on a fixed action category.

Human-in-the-loop fails when the human is asked to do the agent’s job in slow motion. It works when the agent does the agent’s job and the human does the human’s.