"There'll be a human in the loop" often gets said to settle a nervous meeting. What it usually means is that someone will be asked to check whatever the AI produced. Within a few weeks, they're rubber-stamping hundreds of items a day, and the safeguard exists only on paper.
A useful human-in-the-loop is designed, like any other part of the system. It answers three questions: which decisions people make, what information they see when they make them, and how the system learns from what they decide.
Decide what people decide
Not every step needs review. Sort the work by the cost of a mistake and how easily one can be caught. Low-cost, easily reversed actions can be automated fully. High-cost or irreversible ones, such as sending money, contacting a customer, or changing a record of truth, should need a person's decision, not just their glance.
Route by confidence
The system should know when it's unsure. Confidence scores, checks against rules, and simple sanity tests can route clear cases straight through and send uncertain ones to a person. This keeps reviewers focused on cases that need them, which is the only way review stays meaningful at volume.
Show your working
A reviewer who only sees the answer can only agree or disagree. A reviewer who sees the source passages, the extracted fields, and why the case was flagged can make a judgement in seconds. Design the review screen as carefully as the model prompt.
Close the loop
Every correction a person makes is evaluation data. Capture it, look at it regularly, and use it to improve prompts, rules, and thresholds. Over time, more cases go straight through, and people spend their time on the work that genuinely needs them.
- Automation
- Human-in-the-loop
- AI agents
- Workflow design