--- title: Human in the loop AI agents: approval patterns that actually work | Codivated canonical: https://www.codivated.com/insights/human-in-the-loop-ai-agents/ --- [Home](https://www.codivated.com/)/[Insights](https://www.codivated.com/insights/)Governance · 7 min read # Human in the loop AI agents: where people approve, and how By the Codivated team · Updated November 9, 2026 Human in the loop means a person reviews or approves an AI agent's work before it takes effect, at the points where a mistake would cost money, trust or compliance. In practice that is a short list of actions, an approval screen people will actually use, and a log. Most agents start with a person approving everything and earn autonomy action by action. ## What does "human in the loop" mean for an AI agent? It means the agent prepares the work and a person makes the call at defined points. The agent drafts the reply; a person sends it. The agent prepares the refund; a person approves anything over $200. It is different from "human on the loop", where the agent acts and a person monitors and can step in, and from full autonomy, where nobody checks individual actions. | | Mode | The agent | The person | Use it for | Human in the loop | Prepares each action | Approves before it happens | Money, customer-facing messages, record changes, early weeks of any agent | Human on the loop | Acts on its own | Watches, samples and can take over | Proven, low-risk actions at volume | Autonomous | Acts on its own | Reviews reports | Reading, sorting, internal drafts ## Which actions should need a person? Sort every action the agent can take into three tiers: on its own, ask a person, never. The deciding questions are simple: Can it be undone? Does it move money or change what a customer sees? Is there a rule a person would apply that the agent might not? Our [governance guide](https://www.codivated.com/insights/ai-agent-governance/) walks through the tiers in detail. ## What approval patterns work in practice? Our team has built all four of these in production, across customer-facing agents that have taken more than 2.5 million autonomous actions: | | Pattern | How it works | Best for | Approve in Slack or Teams | The agent posts the proposed action with context and Approve / Deny buttons | Internal agents, small teams, fast decisions | Review queue in a dashboard | A queue of pending actions behind your company login, with filters and bulk approve | High volume, back-office work like orders and invoices | Customer approval | Your customers see the agent's proposed work in your product and approve, deny or ask for changes | Agents that act on a customer's behalf, like posting content | Escalation and takeover | The agent works on its own until a rule or its own uncertainty hands the case to a person, who can take over and the agent stops | Conversations, like lead follow-up and support For example, our team built lead-nurture agents that worked conversations on their own and escalated to a person when a lead was qualified or the agent wasn't sure, and a person could take over a conversation at any time. We also built content agents where customers approved, denied or requested changes on every post before it went out. ## How do you keep approvals from becoming a bottleneck? - Show the reason, not just the action. "Refund $84: damaged on arrival, photo attached, within the 30-day policy" gets approved in seconds. - Batch the routine ones. Let a person approve 20 clean matches at once. - Widen by rule, not by feel. When a category has been approved unchanged for a few hundred cases, let it through on its own below a limit. - Track approval time. If approvals wait hours, the agent saves nothing. ## What doesn't work? Putting every rule into the agent's instructions and trusting it to follow them. Past a few dozen rules, models stop following all of them reliably. The rules that matter, like limits, permissions and never-do actions, belong in code that checks the agent's output before anything happens. Focused checking steps can review quality. The approval screen is the last line, not the only one. ## Is human oversight required by law? For some uses, yes. The EU AI Act requires human oversight for high-risk AI systems, and frameworks like NIST's AI Risk Management Framework expect clear roles for human review. Even where it isn't required, an approval step and a log make an agent far easier to trust, audit and improve. First Agent SprintFind and build your first agent in two weeks.A ranked plan, an ROI model and a first working agent in practice mode, for a fixed $15,000. [See what the sprint includes](https://www.codivated.com/ai-readiness-assessment/) ## Common questions ### Doesn't human approval cancel out the time savings? No. Approving a well-prepared action takes seconds; doing the work took minutes or hours. And approval is temporary for most actions. ### Who should approve an agent's actions? The person who would have done or signed off on the work before, at the same level of authority. ### How does the agent know when it isn't sure? Through checks you can measure: missing fields, values outside normal ranges, conflicts between sources and low confidence on a match. Those route to a person automatically. ## Sources - [Article 14: Human oversight](https://artificialintelligenceact.eu/article/14/), EU Artificial Intelligence Act. - [AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework), NIST. - [Building effective agents](https://www.anthropic.com/engineering/building-effective-agents), Anthropic: checkpoints for human feedback in agent design. Written byThe Codivated teamWe build and run AI agents for established companies.[Book a consultation](https://www.codivated.com/contact/) ## Find your first agent in two weeks. [Ask about the First Agent Sprint](https://www.codivated.com/contact/)