Introduction
I keep hearing the term loop engineering. It's all over my feed, every AI newsletter, half the dev videos I open. So I had the same question you probably have. Is this actually important, and do I need to learn it as a frontend or fullstack developer?
I spent some time with it and made a video breaking down what it is and where it fits. This post is the written version, with a practical example you can copy.
What loop engineering actually is
Here's how I think about it. You give your coding agent a goal, and it runs over itself again and again, almost recursively, until that goal is met.
You can also frame it as four parts. You give the agent a trigger or a system prompt with a clear ending state. It acts on that trigger. It observes to check whether the work is actually done. If it hits the stop condition, it stops. If it doesn't, it goes around again.
That's basically it. The idea is that you are defining a goal and a stop condition, then letting the agent close the gap on its own.
It's the agentic loop you already know
If you've built anything with agents, this should feel familiar. It's the classic agentic loop. You give an agent a set of tools, and it uses those tools with its own reasoning to reach an answer.
Say I want to research the latest tech trends. I give the agent a tool to search Google and a tool to search Reddit. On the first pass it searches Google and pulls what it finds. That isn't enough, so it loops again and pulls from Reddit. Then it combines both and hands the answer back to me.
You've seen the same thing in a chatbot. Ask it for the weather and it might call a few tools, look at the context of your question, and come back with what you asked for. Loop engineering is that pattern, pointed at your actual work.
A practical example: fixing a PR on a loop
Let me show you where this earns its place day to day.
I had a pull request that I pushed up using Kiro Web. It connected straight to my GitHub repo, I gave it an action, and it opened the PR for me. You can do this with Claude Code, Cursor, and other agents too.
Then I noticed some tests were failing in my CI/CD pipeline. My GitHub Actions were red. So I gave it one prompt:
Fix all issues on the PR. Keep going until it's all fixed.
That last sentence is the stop condition. Kiro went to work in the background, running again and again, fixing the failures and looping until everything passed. I didn't babysit it through each round.
Tests are a great place to start with this, because the pass or fail signal gives the loop a clear way to check its own work.
Automations are the other big use case
The other place loops shine is on a schedule. Addy Osmani wrote a great article on loop engineering where he points out that automations running on a schedule, doing discovery and triage on their own, are a strong fit for this.
If you can find work that a cron job or a scheduled tool can kick off every day, that's a candidate. A couple of examples:
At the end of every day, run all the tests, and if anything is broken, let the agent fix it. Or keep your documentation current. The loop checks the docs against the latest code and keeps going until they match.
If you use Claude Code, there's a /loop command and a /goal command worth knowing. They let you set up in-session scheduled work, and /goal lets you set a persistent, verifiable objective instead of a single one-off task. You can do the same thing in Kiro and other tools, you just have to be more explicit and write your own system prompts to drive it.
My take
Now the part where I push back a little.
Don't tear up your whole workflow to jam loops in everywhere. There are real spots where they make sense, especially when your agent supports them and you've found an automation worth setting up. But I still think spec-driven development and plain back-and-forth vibe coding will solve most of your problems day to day. Loops are a tool to reach for in the right moment, not a religion.
If you want to go deeper, three people shaped how I think about this. Kent C. Dodds made an excellent video on what he's discovered and how he works this way. Theo did a great one too. And Addy Osmani's article on loop engineering is worth your time.
So I'll ask you what I asked at the end of the video. On your next project, are you going to try loop engineering? Or is it just hype? Let me know.
Resources:
Top comments (19)
I actually never heard of the term Loop Engineering until this post came, and it made sense.
I tried doing that method on occasion but I fear that it will hallucinate midway, which leads to an infinite loop. I had that happen when using Gemma 4 in VScode where it burns up A LOT of tokens and not getting done after waiting for minutes. Was wondering if this is something that can be integrated in the model instead of asking the model to do loop engineering? I know a little about how AI works in depth, so I am not sure if this is something in place.
Hope this makes sense. Great post :D
You can have an additional stop condition that tells the agent to stop if it can't find a solution after x loops. Or something to that effect. I didn't touch on the cost issues, that can be extreme for these type of loops. That's a good point.
The stop condition is where this gets hard in practice. "Observe to check whether the work is done" quietly assumes the agent can grade its own output, but that's the same reasoning that wrote the work, so a confidently-wrong result passes its own check and the loop halts on a false green. The loops that held up for me put the stop condition on something the agent never touches.
I wonder too if using a different model for a loop makes sense, then the model that was used to create the code. To help mitigate that problem...
A different model helps a bit, but mostly it buys you uncorrelated mistakes, not a real check. Two models trained on similar data still share blind spots, so the second one nods along to the confident-wrong answer. What actually moved the needle for us was making the check something neither model can touch: the real diff, a test the coder never saw, or a human sign-off.
Yeah, I guess a human sign off works too
One thing worth watching once tests are the stop signal: the same agent that fixes the code can usually edit the tests too. "Keep going until it's all fixed" is a great instruction, but a stuck agent can reach green by weakening an assertion or just deleting the failing test, and the loop will happily report success. Keeping the tests out of the agent's write path, or at least reviewing the test diff on its own, closes most of that gap. The PR example is still the right place to start though, a red pipeline is about the cleanest stop condition you can hand it.
You're right. Maybe the stop condition needs to be beefed up with more nuance. I know that some people add to their AGENTS.md file additional rules that say that tests can't be just asserted true, and that they shouldn't be fixed, only the code. That could help. Good point.
Interesting perspective! I like how you simplified loop engineering into a practical trigger–act–observe cycle instead of treating it as another AI buzzword. As AI coding assistants become more capable, understanding iterative agent workflows will be valuable for both frontend and full-stack developers. At Dev Technosys, we've also seen AI-assisted development improving productivity, but human oversight is still essential to validate outputs and prevent mid-task hallucinations. Thanks for breaking down a complex topic into something developers can easily understand.
Have you tried out loop engineering?
agentic rag
stopping conditions are the actual design work here. most frontend devs hit this the first time they have to explain why the agent ran forever.
The part that decides whether a loop helps or hurts isn't the loop — it's the stop condition, and "keep going until it's all fixed" is a weaker one than it looks. Tests passing is a proxy for correctness, not correctness itself, and an agent optimizing against a red/green signal will happily satisfy the signal the cheapest way it can: deleting the failing assertion, adding a
skip, loosening a matcher, or catching-and-swallowing the exception the test was there to catch. The loop terminates, CI goes green, and you've shipped a regression with a bow on it.So the real skill in "loop engineering" is writing a stop condition the agent can't game — which usually means the pass signal has to be something it can't rewrite. Coverage gates, a mutation-test threshold, a diff check that flags changes to test files, or a human-approved acceptance step before merge. The moment the agent can edit the thing that grades it, unattended looping stops being automation and becomes an incentive-alignment problem. Worth stress-testing your PR example by pointing it at a genuinely broken implementation and seeing whether it fixes the code or the test.
I recently tried a loop-based approach in an agent workflow (set a goal and let it iterate), but did not get the expected results. The main issue I ran into was that the loop did not have a strong, measurable validation step - so it kept “improving” without actually converging on the right outcome.
I feel loop engineering works best when the success criteria are very explicit and the system has real feedback (tests, logs, constraints) instead of just re-reasoning the same problem. Otherwise it can drift in the wrong direction with more confidence rather than more accuracy.
Great explanation. Loop engineering is most effective when tasks have clear success criteria, such as passing tests or updating documentation. The combination of a defined goal, measurable stop condition, and automated validation is what makes agentic workflows reliable rather than just autonomous.
I’ve seen this fail when the stop condition is just “fix it”. It just spins and burns tokens.
Works better when CI/tests are the only gate, everything else is noise.
Not sure this should live inside the model itself, feels safer kept outside the model loop.