Human in the Loop Patterns for AI Agents

Four human in the loop patterns explained through everyday business examples: approvals, review queues, handoffs and decisions that stay with a person.

30 July 2026 · 5 min read · by the Yoonet team · Updated 12 September 2026

If your business has automated enquiries, orders or document handling, you have probably found a few places where a person still needs to step in. The useful question is when that should happen, and what the person should be responsible for.

Human in the loop patterns are ways of organising those checks. These four approaches cover common business situations. You can use them to brief your IT provider and agree the work with the people doing the reviewing.

1. An approval gate: check before the action happens

An approval gate pauses an action until an authorised person agrees. For example, an automated customer-service process might prepare a refund, but refer an amount above your agreed limit to a manager before issuing it.

This is useful when a mistake would be difficult to reverse. The reviewer needs to see the proposed action, the reason for it and the relevant policy. They should be able to decline it or ask for more information.

# The agent proposes; a person disposes.
result = agent.run(task)
if result.risk >= THRESHOLD:
    decision = ask_a_human(result)   # blocks the action, not the agent
    if not decision.approved:
        return result.cancel(decision.reason)
result.execute()

This simplified example is for your technical provider. The business decision comes first: which actions need approval, who can approve them and what happens if nobody answers. Avoid sending every routine action for a tick; reviewers need time to read the cases that matter.

Our integration guides show the same idea in LangChain and LangGraph and the OpenAI Agents SDK.

2. A review queue: check a defined set of work

A review queue is a list of outputs waiting for someone to check. For example, a team member could compare extracted invoice details with the original documents, or review a sample of AI-drafted replies.

Decide whether the work can be released before that check. Low-risk sampling may happen afterwards. If an incorrect output could cause a serious problem, hold it for review first. A queue alone does not decide that policy.

# Sample or filter outputs into a queue a person works through.
for output in agent.outputs():
    if needs_review(output):        # confidence, novelty, stakes, or a % sample
        queue.put(output)
    else:
        ship(output)

In this simplified example, selected outputs go to the queue while others continue. Agree who owns the queue, how quickly it needs attention and how corrections get back into your system. Review the number of outstanding items as well as the quality of completed checks.

If your existing team cannot cover that responsibility consistently, our build-or-buy guide explains the staffing choices.

3. Escalation: let a person finish the job

Escalation means handing over a case that automation cannot resolve. A supplier may not respond to email. A customer may need an exception to the usual delivery process. A record may contain two equally plausible addresses.

This is more than an approval. The person may need to read the history, make a call, ask a question and decide the next step within their authority.

A useful handoff includes the case history, what has already been tried, the rule to follow and the result required. It should also say who to ask if the case is outside the reviewer’s authority. Our business examples show the kinds of work involved.

4. A human-owned decision: keep responsibility with the right person

Some decisions should remain with a designated person even when software does much of the preparation. The system can gather documents and summarise a case, while a manager makes the final decision.

The important part is a record of who decided, what information they used and why. A button labelled “approve” does not establish that the reviewer had the right authority or understood the case.

Your business sets those responsibilities. A support team can prepare and check the information, then refer the decision to your nominated person. Our guide to managed human validation explains how to agree that division of work.

Who will actually do the checking?

A technical demonstration often uses the developer as the reviewer. In a business, that role needs working hours, instructions, training and cover when someone is away. Otherwise, each unanswered request returns to the owner or the same busy team member.

You can assign the work internally or discuss an employed support team. At hitl.ph, we work with Australian and New Zealand businesses to agree the process and the people behind it. Our team in Balanga is employed and managed by Yoonet.

Your provider can use the API documentation to discuss the connection. Start by deciding what needs checking and who can decide. The software should support that arrangement.

Make this work in your business.

A short conversation about your business is a good place to start. Tell us what you have automated, what still needs checking and how often it happens.

You can email us directly at ai@yoonet.io.