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LLM /human-in-the-loop

Human in the Loop

A design where an automated or AI system pauses at chosen points for a person to approve, correct, or reject, before it continues with a consequential action.

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The useful mental model

Decide which actions matter, then pause only there. The system declares that a step needs approval and stops with the pending action visible. A person approves or rejects it, and the run resumes from where it stopped. The skill is choosing the gates: sending an email, spending money, changing a record, running a command. Gating every step defeats the point of automation, and gating nothing leaves the agent unsupervised.

In plain English

It is a checkpoint with a person at it. The agent does the work, stops at the risky step, shows what it is about to do, and waits for a yes or a no.

Where builders use it

  • 01Approving a refund or an outbound email
  • 02Reviewing a code change before it is applied
  • 03Signing off a page before it is published

Why it matters

An agent can be wrong in a way that is hard to undo. A person at the right gate turns a possible mistake into a request that can be read and declined, and leaves a clear record of who approved what.

Why now

Agents now take actions through tools, and frameworks add built-in approval pauses and ways to resume a paused run. Practices differ between products and change quickly, so check current documentation before relying on a specific behavior.

A common misconception

“Human in the loop means a person watches every step.”

It means a person decides at chosen points. The guidance points to consequential actions such as sending emails, cancelling orders, running commands, or editing files, not to every step.

“An approval prompt makes the action safe.”

An approval only helps if the reviewer sees the real action and is authorized to decide. The documentation warns that saved run state should come from trusted storage, and that the server should check who the reviewer is and validate their decision.

See

See the mental model

The visual is generated from the same structured knowledge as the written explanation.

Visual modelsequence

A five-step sequence: the agent works until a gated step, the run pauses and shows the pending action, a person approves or rejects it, the run resumes, and the decision is recorded.

Pause at the actions that are hard to undo, and let the rest run.

Builder example

Pause where it matters, not everywhere

An assistant can read orders, draft replies, and issue refunds.

Failure

Either every step needs a click and the team stops using it, or nothing needs approval and a wrong refund goes out before anyone sees it.

Intervention

Let the assistant read and draft freely, and mark only the refund and the outbound email as needing approval. When it reaches one, the run pauses and shows exactly what it will do. A signed-in reviewer approves or rejects, and the run resumes.

Observable result

Routine work flows without interruption, and every refund and email passes a person who sees the exact action first.

Sources

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Last researched 12 October 2026