🔧 Master Loop Engineering: Everyone is Talking about This for Claude as The Next Big Thing!

Loop Engineering transforms Claude from a single-task AI into an agentic workflow powerhouse. Easily set up loops to let Claude execute multi-step tasks.. Ai Tools, 🔥 Ai Fire Academy, Ai Automations.

TL;DR

AI automation works best as a loop: a task that runs repeatedly until it actually finishes, checks its own work, and remembers what failed. Use AI automation when a task repeats, has a clear finish line, and can be checked automatically.

Claude Code offers 3 ways to trigger AI automation: /loop for quick checks in a session, Cloud Routines for work that runs without your laptop on, and Desktop tasks for local file access.

Good AI automation only works if the skill inside it has already been tested, and if the goal comes with a real way to verify the result.

Key points

Anthropic’s Code Review costs $15-25 per run and takes about 20 minutes.

Common mistake: building AI automation around an untested skill, which just repeats the same error faster.

Practical takeaway: run new loops supervised first, then turn off supervision once you’ve seen it succeed several times in a row.

Introduction: Loop engineering is having a moment

Boris Chernythe engineer behind Claude Code, said something recently that should change how you think about AI for good:

Nvidia’s Jensen Huang: “Nobody writes prompts anymore. The new job is to write and handle loops.” Then I found Peter Steinberger saying almost the exact same thing:

There’s endless AI hype out there. But when two people who literally build the tools we’re using both land on the same idea, independently, I pay attention.

So I went deep on loop engineering. And I found that it’s actually really simple. Most people just overcomplicate it.

In this post, I’m breaking loop engineering down so everyone inside AI Fire community can get it & apply it to your current workflow.

AI-generated Podcast: Spotify | Apple Podcasts, YouTube.

I. Four-Condition Test: Should Your Task Be a Loop?

Not every task deserves a loop. Before building anything, run it through 4 questions.

Does the task repeat? If it only happens once, a prompt is enough.
Is there a clear definition of done? A loop needs a finish line it can recognize on its own, not a vague feeling.
Can you accept the cost? A loop runs through multiple steps, not a single reply, so the cost adds up fast.
Does the loop have the tools to finish the work and check its own result?

To make this easier to picture, here is a real example from Anthropic itself. They have a feature called Code Review, built to check code automatically before it gets merged into the product.

It’s a good case study because it hits all 4 conditions cleanly.

the-four-condition-test-for-using-a-loop-1

1/ Repeats? Yes, this task repeats constantly, since dozens of new pull requests show up every day.

2/ Clear finish line? Yes, what “done” means is very clear: find the bugs in the code and rank how serious each one is, not just “skim through it.”

3/ Cost is known upfront? Yes, Anthropic states each review costs around $15–25 and takes about 20 minutes to finish, so anyone using it knows the price upfront instead of guessing.

4/ Can check its own work?

Most importantly, it uses several AI agents that analyze the same piece of code together, cross-check it against the surrounding code, and confirm the bug is real before reporting it back to the person.

If something you do is missing even one of these 4 things, do not force it into a loop. Doing it by hand or with a normal prompt is still the better choice.

II. Four Essential Parts of a Loop

A loop works because four parts fit together.

Trigger: the event that starts the loop, on a schedule or tied to something that happens.
Execution skills: the part that actually does the work, built on a skill that has already been tested.
Goal and Verification: a specific target, paired with a way to check whether that target was actually hit.
Output and Memory: the result you can see, plus a record that keeps the loop from repeating the same mistake.

Looking at Claude Code Review, the feature Anthropic uses itself to check code before it gets merged, you’ll see how these four parts work together.

→ Trigger: if the repository is set to run automatically, the loop fires the moment a new pull request opens or a new push lands, no one has to press anything.

If the repository is set to Manual, someone has to comment @claude review before the loop runs.

→ Execution skills: the work does not go to one single AI. Several agents analyze the code and the surrounding context in parallel, each one looking for a different class of issue. A separate step then checks those findings before ranking them by severity.

→ Goal: the target here is catching real bugs before the code gets merged. This is my own read on the feature’s purpose, not a direct quote from the source.

→ Verification: a dedicated step checks each finding against how the code actually behaves, filtering out false alarms before anything gets reported to the user.

→ Output: you see the comment sitting right on the line of code with the problem, no searching needed.

→ Memory: each project’s rules live in a file called CLAUDE.md. Every time a review runs, the system reads that file, so if new code breaks a rule already written down, it catches that on its own. No one has to repeat it.

III. Step 1: Know How to Set Up the Trigger Loop

A loop needs something to start it. Without a trigger, nothing fires, and you’re back to typing prompts by hand.

We’ll suggest these three ways for you to set this up, each one fits a different situation.

Option	How it works	Best for
`/loop`	Runs right inside the session you’re already using. Stops the moment you close that session, and even if you leave it running, it expires on its own after 7 days	Quick checks done at a short pace
Cloud Routines	Runs on Anthropic’s own servers, not tied to your machine. Close your laptop and the loop keeps going	Anything that needs to run overnight or at a fixed time every day
Desktop tasks	Runs in the background right on your machine, so it can reach your local files directly, but your computer has to stay on	Work that needs local file access

If you want something that checks a website every five minutes while you are working, /loop is enough.

If you want a report generated every morning at 8am whether or not your laptop is open, Cloud Routines is the right pick.

IV. Step 2: Build Strong Skills, Goals, and Verification

A loop is only as good as the skill running inside it. If you put a weak, untested skill into a loop, you just get the same mistake repeating faster.

Anthropic found proof of this while building Claude Code. Verification skills, the ones that check whether work actually succeeded, made the biggest difference to output quality out of everything they tried.

There’s this example checks a checkout flow with test cards, then confirms the invoice lands in the right state:

building-strong-skills-goals-and-verification-2

That example shows why a goal alone is not enough.

Telling Claude to “fix the bug” gives a direction, but no way to check the result. Pairing it with a real verification step, a test, a log check, a comparison against an expected state, is what makes a loop trustworthy.

You have a few practical ways to build that step:

A script or test that returns pass or fail, nothing to interpret.
A second pass, like a separate skill, that checks the first output before anything ships.
A scoring rule, where Claude rates its own result against a standard you wrote down ahead of time.

Don’t skip this part, otherwise your loop will report success even when nothing actually got fixed.

V. Step 3: Generate Output and Memory

Output is what you see after the loop finishes running. It could be a file, a report, or a fixed bug. If there is nothing to see, you do not know whether the AI automation actually did anything.

Memory is the part most people skip when they are new to AI automation.

Picture it this way: if a loop has no memory, every time it runs, it acts like a brand new person who knows nothing about the last run. It can walk straight into the same path that already failed before, without knowing it ever tried that path at all.

Real Case: Multi-Day Science Projects

Anthropic ran into exactly this while using Claude Code for science projects that took many days, like this.

A researcher had Claude build a complex numerical formula across many separate work sessions. To keep the automation from repeating old mistakes, they had Claude write its own progress file. That file tracked three simple things:

What was already done
What was being worked on
Which approaches had already been tried and didn’t work

Because of that, the next work session didn’t walk back down a path already known to be wrong.

That’s the real job of memory, not just remembering what worked, but also remembering what failed, so the next run doesn’t waste time trying it again.

A few simple ways to do this:

A file that logs what ran, what happened, and any errors hit along the way.
A separate file that lists what failed, updated after every run.
A short note Claude writes to itself, summarizing what changed since last time.

Without memory, any AI automation you build is like someone who loses their memory every day, starting over again and again with no end in sight.

VI. Complete Map: How to Build Your First Loop

Start with the smallest task you have already done by hand, one you know works well, and one whose every step you already understand.

4 steps to build your first loop for your AI automation:

Pick a small task that repeats. Skip the whole process for your first try. Pick something you have done many times, know each step of, and know when it is “done.”
Use a skill you already trust. Hold off on putting an untested skill into a loop. If you don’t have one yet, run it by hand a few times first, check that it works, then turn it into a loop.
Package it into a full skill. A good skill carries a clear goal, a way to verify the result, and a plan for what to do when something goes wrong.
Start with supervision, then move to automatic. This is the step most people get backward when building AI automation.

Anthropic found this same pattern by tracking how Claude Code users change their behavior over time: new users tend to approve each action before Claude takes it, while experienced users let Claude run many steps in a row and step in only when needed.

Follow that same path with your own loop: run it supervised the first few times, check the result at each step, and turn off supervision only once you have seen it work correctly several times in a row.

If your task still feels vague, if “done” isn’t clear yet, hold off on the loop. Break it into smaller pieces and add a checkpoint in the middle, so you keep a chance to step in if it goes the wrong way.

If you want to see a real loop built from start to finish, I’ve placed a video walkthrough right below.

Conclusion

A loop gives the work you already trust a system that can keep running on its own.

Start small with a task that repeats, one you already know how to do by hand. Build the skill first, test it, then wrap it inside a loop once you trust it.

Once the skill is solid, the next step is giving the loop a way to know when it is done. Pair every goal with a real way to check it, and write down what failed, not just what worked, so the next run doesn’t walk the same dead end twice.

Even with the skill and the goal in place, run the loop supervised first. Loosen that grip only once you have watched it succeed several times in a row.

That is the shift AI automation asks of you: stop retyping the same prompt every morning, and start building the system that keeps the work moving on its own.

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