Human in the loop
Human in the loop means a person reviews, approves, or corrects an AI system's output at some point before it takes effect.
Category
Governance
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What is human in the loop?
Human in the loop describes a system where a person reviews, approves, or corrects an AI system's output at some point, instead of letting the system act fully on its own. That checkpoint can sit in different places: before an action happens, as an approval step, or after, as a correction that feeds back into how the system behaves next time.
This solves a real risk: an AI system can be confident and still wrong, and without a person checking its output at some point, a bad decision can take effect before anyone notices. Human in the loop keeps a person positioned to catch that before it causes damage.
How does human in the loop differ from full automation?
Full automation lets a system act the moment it decides something, with no pause for anyone to check the decision first. Human in the loop inserts a deliberate pause: the system proposes an action, and it only takes effect after a person approves it, or a person corrects it before it goes further. The tradeoff is speed for oversight, full automation moves faster, human in the loop catches more mistakes before they matter.
How does human in the loop work in practice?
In practice, human in the loop usually means the system produces a draft, a suggestion, or a proposed change rather than a finished action, and routes that draft to a person before it becomes final. The person can approve it as is, reject it, or edit it, and that decision either lets the change through or sends it back.
Qontext builds this in through change reviews: an agent proposes a drafted or suggested change to the context repository, and a person can approve, reject, or edit it before it's reconciled into the repository that every other agent reads from.