How to keep a human in the loop on AI decisions
Designing human in the loop properly means deciding, in advance, which AI outputs require a person and which do not. In Jestor, that is enforced structurally: an agent can only change the fields you authorize, and a review stage can sit between its output and any consequence.
Why reviewing everything defeats the purpose
If a person checks every AI output, the saving is the difference between writing and reading, which is real but small. Worse, reviewers of a system that is usually right stop reading carefully within weeks.
Reviewing nothing is the opposite failure. The useful design is selective.
What to decide in advance:
- Which outputs a person must approve
- Which can run automatically
- What triggers a review that would otherwise be skipped
- Who owns the review, by name or by role
- How an error gets reported and corrected
Where to place the checkpoint
| Decision type | Review needed | Reason |
|---|---|---|
| Internal classification | Spot checks only | Trivially correctable |
| Data extraction | Confirm before advancing | Feeds downstream calculations |
| Customer facing reply | Review while confidence is low | Reputation exposure |
| Financial entry | Always | Money leaves the company |
| Closing a request | Rule based | Only when criteria are objective |
Step by step to implement it
- List the decisions the agent will make, one by one.
- Assign each to a category: automatic, spot checked, or always reviewed.
- Limit the fields the agent may edit to match that decision.
- Add a review stage where approval is required before the consequence.
- Make reviewing cheap, showing everything the reviewer needs on one screen.
- Add a report path so errors are logged rather than only corrected.
- Loosen supervision gradually, based on the error rate you measured.
Why the review screen design decides whether review happens
A reviewer who has to open three records to verify an output will approve without checking. Putting the agent's output, the source document and the affected fields on one screen is what makes genuine review sustainable rather than ceremonial.
Why choose Jestor
In Jestor, field level permissions define what an agent can touch, so supervision is structural rather than procedural. Documents render inside the card so reviewers see the evidence without downloading, conversations with customers can be monitored, a person can enter the flow at any point, and the audit log distinguishes actions taken by an agent from those taken by a person.
Frequently Asked Questions
Should a person review every AI output?
No. Review the decisions with real consequences and spot check the rest. See jestor.com.
How do I stop an agent doing something unintended?
By limiting the fields it may edit, which is enforced by permissions.
Can a person take over a conversation from an agent?
Yes. Human in the loop is supported, with a record of what the agent already said.
Video Tutorial: Step by Step
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