Loops can be a valuable tool to help you automate your work, but sometimes a better prompt is good enough. Here’s how to tell when it makes sense to build a loop vs a bit more AI prompt engineering.
TL;DR
Any recurring task can be turned into an AI agent loop, but setting one up makes sense when four things are true. You need to repeat the task often, the output work can be compared against something like a rubric, the cost makes sense, and the loop knows when to stop on its own. Miss any of these and you might be better off trying to write a better prompt.
Over the past few years, as we’ve learned how to better interact with LLMs, we’ve silently been building up our internal prompt library. Maybe you’ve done the same, whether in Notes, Notion, or maybe even Github. But in the past month or so, every other LLM influencer has been touting the benefit of writing fewer prompts and more loops.
But what does this even mean?
To put it simply, building a loop is not doing less work, but it’s steering your LLM agent to do the prompting and prompt tweaking, rather than you doing it yourself. It’s like using adaptive cruise control to help you keep your distance, rather than doing it yourself. You’re still giving instruction, and you’re still responsible for steering and driving, but some of the work is offloaded to the car, based on certain criteria.
Building an agentic loop is getting the system to check itself and iterate until it reaches your “destination”.
A loop can be as small as a checklist you run inside a normal chat, no code required, or as built out as a scheduled automation that runs on its own. Same idea at both ends, only the setup differs.
The difference is not the prompt. It is who keeps the work on track. Prompt by hand and that job is yours on every pass. You catch the errors, you hold the standard in your head, you decide when it is done. A loop takes those jobs off you and hands them to the system.
Loops can work for anyone, not just developers. Often it can be more complicated for non-development tasks because the evaluation criteria is not cut and dry. Here’s what we’ve learned is necessary to make sure you’re using AI loops correctly.
Start with the work, not the tool
Before reaching for a loop, look hard at the work itself. Are you feeding the AI the same context every time? Applying the same checklist? Fixing the same mistake? Asking the same follow-up? Repetition is the clue. But it doesn’t mean you need a loop. It means there may be some pattern worth investigating. To know if you have a good candidate, follow this four-point test.
1. You constantly have deja vu
If you find yourself asking your agent to do the same thing over and over, it might be a good time to think about building a loop. Keep track of what you’re asking to be sure, as the frequency you have to ask it, and the time it takes you to process the answer because that is the time you will save with a loop.
2. You have some objective measure
Loops can’t work in all situations, because AI agents aren’t always the best judge. If your task has some specific criteria to make a call, like checking for specific keywords or call to actions, then an agent and a loop can handle this check for you. If it’s something that requires your gut reaction, then making a loop and having the agent try to use its gut, doesn’t always end well.
One thing to note is that you don’t need to get this check perfect at the start, just make it strong enough to reject bad work, and iterate on the criteria as you go along.
3. The cost makes sense
This is what I was alluding to in the first step. You have to calculate the amount of time saved vs the time it takes to create and refine this AI agent loop. Days spent making a loop doesn’t justify the time cost if it only saves you a few hours each year. To calculate your cost (and thus potential cost savings) calculate how long it takes you to complete the task, multiplied by how many times you do it (in a given period of time).
Cost = Time required to do the task once × Number of times the task is repeated
The potential cost savings becomes the max amount of time you can dedicate to build and refine the loop. If you can’t honestly say you will get an ROI for building the loop, just do the task by hand and move on.
Aside from time, the second real cost for running a loop is the computation cost. If you’re creating this with a monthly subscription, it may not matter to you if you’re not hitting the max limits often, but if you’re on API pricing, then the cost can quickly add up, especially if there’s high token consumption by your loop. This may or may not be a deciding factor, so just be aware if the token costs are itemized or not.
4. It knows when to stop
A good loop knows when to finish and when to ask for help. Underneath, every loop comes down to the same three parts, a trigger, an action, and a stop condition.
If your task has a natural end, like the last item on a checklist or the final slide in a deck, then your loop can simply end there. But if each iteration keeps improving the outcome, albeit with shrinking gains, you can cap it at a set number of passes. Otherwise you’re spending extra money on runs that barely move the needle.
Stay with a prompt when judgement leads
Prompts do not stop being the right tool once you get more advanced. For a whole class of work, a plain prompt beats a loop, even work you do all the time. Here are two kinds of prompts that should always remain prompts.
The first is anything that will not repeat in the same form, like the one-off board memo, a sensitive announcement you send once, or the founder quote that has to sound like one specific person.
The second is anything the AI cannot reliably check, such as a rebrand direction, a delicate pricing call, or a positioning statement for a product that does not exist yet. AI agents cannot replicate your sense of judgement (without a lot of context), so pushing it to a loop would just generate lots of extra work for you to review.
What a loop can’t do
It can’t invent a good standard. If the team cannot say what good output looks like, a loop will not fix that. It may only make the confusion faster. For brand, content, and campaign work, the standard has to come first. That means the messaging guide, the approved claims, the citation rules, and examples that show what good means here.
It can’t take on accountability. Whoever ships the work still owns it. A loop can catch missing sources, weak structure, or obvious drift. It cannot answer for a legal claim, a brand decision, or a sensitive client judgement.
It can’t make every task cheaper. Some loops cost too much because they run too many passes. Some because they produce too much to review. Some because nobody uses them twice.
How to get started with a loop?
Here’s a handy brief to get started with your first loop. Write it down by hand before trying to talk to an agent, and start with something routine and small so you can see what kind of prompting is required for your agent of choice.
Goal: what should be true at the end?
Input: what material should the AI use?
Action: what should it do on each pass?
Check: how should it judge the result?
Stop: when should it finish or ask for help?
Run it in a normal AI chat and watch where it breaks. Tighten the check, add examples, strip out the ambiguity. Only then decide whether it deserves to become a saved prompt, a project instruction, a custom workflow, or a scheduled automation.
Here’s an example of a loop from our content team:
Goal: Publish-ready blog post. No unsupported claims, no generic phrasing, voice matches our style guide.
Input: The attached draft and our editorial style guide.
Action: Revise the draft. On each pass, tighten the language, cite or cut unsupported claims, and remove any phrase that appears generic or AI-generated.
Check: After each revision, score against this checklist. Every factual claim is cited or marked as an assumption. Sentences are short and direct, with no filler. The opening ties to a specific business problem. Nothing reads like it was pulled from a template.
Stop: If every item passes, return the final draft. If any item still fails after three passes, stop and list what is unresolved.
We ran that loop on the draft of this very article. What you are reading is the result, with a round of human editing on top.
What’s next?
Haven’t gotten your fill on loops? Us neither. This will be the first of a three-part series on AI loops. Next we will look at how to design the check itself, then how to graduate a loop from a chat window into a scheduled automation. Subscribe to our newsletter to ensure you get the latest insights delivered to your inbox.
And if you need help building loops or working with AI agents, Hire Digital can help you manage and scale with specialised AI experts in your industry.