An AI loop is a repeatable piece of work with a clear definition of done, delegated to an AI agent that keeps working and checking until that definition is met.
The agent runs the loop. A human owns its specification and is called when the work reaches a genuine exception, a decision the organisation has not made or a protected boundary.
Human intervention is a fallback or decision path, not part of routine completion.
If you're rolling out AI across your organisation and you're not especially technical, loops are the thing to learn. Not agents.
Loops aren't the next thing in AI. They're THE thing when it comes to adoption at scale throughout the organisation.
Agents are tactical. Loops are strategic.
Agents are the implementation. The loop specification defines the work: what it's called, when it starts, how it knows it's done, what becomes real, what it can use and the boundaries it works within.
So if you're a leader managing a team, rolling out AI not just for yourself but across a team or an organisation, loops are the skill to build.
Unless you're going to physically develop an agent yourself, I wouldn't spend much time learning the mechanics of agents. Start upskilling in loops instead.
I've written up the full method in the six-question AI loop specification. That's the practical starting point.
The anatomy of an AI loop
Every dependable loop has the same parts:
- An observable trigger. An event, state change, threshold or schedule the agent can actually see. "When somebody remembers" is not a trigger.
- A definition of done. A checklist of conditions the agent can verify before it may finish.
- Working and checking. The agent keeps going, checking its own work against that definition, until it's met.
- A human owner. One person accountable for the specification, the rules and what the business learns. Not a committee.
- Available capabilities. The tools, data, templates, agents and sub-loops the agent can use to complete the work.
- Guardrails. Where the agent should act, when it should ask its owner, and when it must stop.
If a person has to prompt, review and redirect the work every few minutes, you don't have a loop yet. You have an AI leash.
What a loop looks like in practice
Take Meeting to Action, the loop I use most often to explain the idea.
It starts when the meeting transcript arrives. It finishes only when every action has an owner and date, decisions are recorded in their authoritative home, unanswered questions are visible and follow-up has been sent.
When it's done, participants and the system that runs the work share one accurate account of what happens next.
Notes are produced along the way. But notes aren't the outcome; the changed state of the work is. I've written that one up in full in How do you turn meetings into accountable action with an AI loop?
Loops can be small or big
A loop can be small and run over a few hours.
A loop can be big and run over weeks or months.
Either way, the thing you're designing is the same: a piece of work with a clear start, a testable finish and a real business state it creates. There are twelve worked examples here if you want to see the range.
What this changes for leaders
We're not going to design processes, governance policies or standard operating procedures the way we always have.
Instead, we're going to design loops.
That's the mindset shift for anyone leading an AI rollout. Stop writing SOPs for humans in isolation. Start specifying work clearly enough that an agent can run it, and a person can own it.
If you're leading an AI rollout, learn to design loops. That's the strategic skill that scales adoption; agents are just the tactical pieces inside them.
If you want to get practical with this, I run a 60-minute AI Loops workshop on how to scale AI across teams and departments to drive organisation-wide adoption. It's free and hands-on. Come along.
