Human-in-the-Loop Controls for Agentic AI Workflows
Human oversight is useful when the reviewer has clear authority, suitable evidence, enough time and a defined action. A vague approval button is not a control.
Decide why a person intervenes
A person may supply missing context, make a value judgement, authorise a consequential action, resolve conflicting evidence or respond to an exception. Name the purpose of each intervention and the role accountable for it.
Place review before the material consequence
Approval should occur before a payment, customer promise, access change, adverse decision or safety action. Reviewing a summary after the system has acted provides oversight evidence but does not prevent harm.
Give the reviewer usable evidence
Show the proposed action, supporting sources, important uncertainty, policy rule, prior steps and consequence of approval or rejection. Do not require the reviewer to reconstruct the case from a long conversation trace.
Design thresholds from observed results
Route cases using consequence, ambiguity, novelty and validated model performance. A confidence score alone is insufficient unless it is calibrated for the actual task and population.
Protect against automation bias and review fatigue
Sample accepted cases, rotate reviewers where appropriate and measure override quality. If people approve almost every case under time pressure, the control may exist only on paper.
Record decisions and feed learning back
Capture approval, rejection, edit, reason and later outcome. Use reviewed cases to improve evaluation sets, process rules and training. Do not treat every override as proof that the human or the agent was correct.
Preserve manual continuity
Define how work proceeds when the agent, tool or model is unavailable. Staff should be able to identify queued cases, resume from a known state and prevent duplicate action.