Human-in-the-loop
Human-in-the-loop is a way of working in which the agent proposes and a person approves before anything consequential happens. Instead of letting the model act on its own, a human stays in the decision path, reviewing, correcting or signing off on its output.
The reason is the nature of the model itself. Output is non-deterministic, can be confidently wrong (a hallucination) and can drift toward telling you what you want to hear (sycophancy). A human in the loop is the check that catches the cases automated grounding and review do not.
Human-in-the-loop is not all-or-nothing. The right amount of oversight depends on the stakes: low-risk, easily reversible tasks may need little or none, while decisions that are costly, public or hard to undo warrant a human signing off every time.
When to keep a human in the loop
- High stakes: outputs that are costly, public-facing or hard to reverse.
- Low confidence: answers about recent or fast-changing facts, or topics near the model's knowledge cutoff.
- Sensitive judgement: decisions that need accountability or domain expertise a model cannot supply.
Human-in-the-loop in Rational AI
The platform is built so that AI work can be inspected rather than taken on trust. Grounding answers in your Knowledge through RAG narrows how far output can drift, and every conversation through a touchpoint can be reviewed, keeping a person in the loop wherever the stakes call for it.