Human in the Loop
Human in the loop means keeping a person in the decision path of an AI system — to approve high-stakes actions, review uncertain outputs, or label the cases the model got wrong. It's the practical way to deploy autonomy you don't fully trust yet.
Also known as: HITL, human-in-the-loop
Enterprise AIAI Evaluation & Reliability
Human in the loop is the design choice to keep a person in the AI’s decision path rather than letting it act fully on its own. The person might approve an action before it executes, review outputs the model flags as uncertain, or correct the cases it gets wrong so they become training and evaluation data. It’s how teams ship useful automation without betting everything on the model being right.
The skill is placing the human where it counts. Route everything to a person and you’ve lost the automation; route nothing and you’ve shipped unreviewed autonomy. The usual answer is to gate by stakes and confidence — auto-handle the routine, high-confidence cases and escalate the risky or uncertain ones. Over time, as evaluation shows the model is reliable on a slice of work, you widen what runs without review. The loop isn’t permanent scaffolding; it’s how you earn the trust to remove it.