Guide
Human-in-the-loop review for AI agents: how to set it up
Human-in-the-loop review for AI agents means an agent's result is a proposal until a person approves it. A useful review gate records who approved what, against which checks, and turns a rejection into the instruction for the next revision, so review is part of the work rather than a message sent somewhere else.
Why agents should not approve their own work
An agent can be confident and wrong in the same sentence. When its output goes straight into a contract, a customer reply or a pull request, the first person to check it is often the person it was sent to.
The rule that fixes this is simple to state: an agent proposes and a person approves. The agent never marks its own work finished.
What a review gate needs
- Versioned results, so a reviewer knows exactly which draft they approved and can compare it with the last one.
- A checklist that fits the work: a contract review checks different things from an incident write-up or a sales email.
- A recorded decision, with who approved it and which checks they confirmed, kept next to the result.
- A send-back that carries a note, where the note becomes the agent's instruction for the next version.
Choosing what reviewers check
A checklist works when every item is something a reviewer can actually confirm by looking, such as "every figure has a source" or "no customer data appears in the draft". Items like "quality is good" get ticked without being checked and teach people that the gate is a formality.
Keep the list short and specific to the kind of work, and make it required. A gate that can be skipped will be skipped on the day it matters.
Review on a team, not only by one person
When the agent's session is shared, the reviewer can see the whole run that produced the result: the plan, each step, the sources and the directions people gave. That makes review faster and more honest than reading a finished document with no history behind it.
How Vrittara does it
Every Vrittara session ends in review. The agent produces a versioned result and a teammate approves it or sends it back with a note, which becomes the instruction for the revision. Each team playbook carries its own review checklist, and the checks must be ticked before a result can be approved. Approved results can be kept in the team's shared memory, together with the directions that shaped them.
Related guides
Common questions
- What does human-in-the-loop mean for AI agents?
- It means a person reviews and approves an agent's output before it is used. The agent proposes, a person decides, and the decision is recorded.
- What should a reviewer check in AI-generated work?
- Checks that fit the work and can be confirmed by looking: sources for every figure, no private data, the right audience, terms that match the agreed position. A short required checklist works better than a long optional one.
- What happens when a reviewer rejects an agent's result?
- In a well-built review gate the reviewer sends it back with a note, and that note becomes the agent's instruction for the next version, so the revision addresses exactly what was wrong.