The Human in Human in the Loop

What a good loop looks like

Human in the loop works when people are asked to do human things: make judgment calls, accept risk, and correct edge cases. It fails when they are asked to rubber‑stamp.

Define decision rights

For each automated action, specify who can approve it, what evidence they see, and what happens if they reject. Decision rights prevent approval theater and clarify accountability.

Show your work

When the system proposes an action, surface the facts and the path taken—retrieved documents, tool calls, and confidence scores. Explanations do not need to be academic; they need to be actionable.

Reduce cognitive load

Present one decision per screen with sensible defaults and keyboard‑friendly controls. Batch similar decisions and let approvers set rules (always approve under X conditions) that the system can apply next time.

Make exceptions meaningful

Require a reason code on rejections and feed that back into evaluation and training. A small taxonomy—missing data, policy risk, wrong recipient—is enough to make your loop learn.

Design for latency budgets

For low‑risk actions, asynchronous approvals with timeouts keep flows moving. For high‑risk moves, synchronous approvals are worth the wait. Align the loop’s speed with the risk profile.

Keep an audit trail

Immutable logs of proposals, approvals, and overrides protect users and the business. They are also valuable for debugging and compliance.

Train the loop, not just the model

Review samples weekly: where did humans add value, where did they not? Simplify the cases that should be auto‑approved and improve the guidance for the ones that need eyes.

Bottom line

The right loop increases trust and throughput at the same time. The system handles the common case; humans handle the uncommon case; both get better together.

Sample approval flow

Quality safeguards