All The Types Of Claude Loops Explained In 13 Minutes

Claude loops, loop engineering, and the 5 agentic loops we run in real AI coding.
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Claude loops can save your tokens or burn through them, it depends on the agentic loop you pick. This Claude Code guide breaks down the Claude Code loops we actually use, the loop engineering behind these agentic loops, and how each loop fits your workflow.

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So what are Claude loops, really? Everyone is hyping loops claude builders keep talking about right now, but most people run the wrong one for the job and just watch their usage limit disappear. We're a software company, and we've been mapping out these loops inside our own ai coding, figuring out which use case each one is actually built for. This is loops claude code the way it holds up on real work, not a demo.

Before the types, we recap what loop engineering is: you stop being the person writing the prompts that drive the agent and turn it into a system that runs the loop itself, learning as it goes. Then we walk through five different claude code loops we've set up, how to build each one, and how it changes your workflow.

Here's what we cover and how we actually use each loop:

Stateless loop:
No memory, no self-improvement. It runs the task, checks itself, and stops once it's done. The Ralph loop and the Goal command are the clearest examples of this claude code loop.
Works best when you can check the work in a hard, concrete way. We write tests first, set the goal to pass every test, and let Claude write code and rerun the tests until they all pass.
One run took around 39 minutes and burned through more than 200k tokens, so context usage is the thing to watch before you set up any claude loop.

Learning loop:
Instead of finishing and stopping, it improves a skill or workflow you'll reuse. A skill improver agent runs the skill with and without it to measure the real impact, then makes changes directly.
The key part is a learning.md file that lives inside the skill, an improvement journal of what it tried and what worked, round after round.

Multi-agent loop:
A review is never one aspect, so we split it across four agents (factual, domain, safety, and style) coordinated by an orchestrate command. Close to Karpathy's LLM Council idea, where multiple agents cover the blind spots one agent would miss.

Verification loop:
One agent implements, another scores it against a set metric, and the implementer's whole job is to push that score as high as it can go. We built this around Cursor's thermonuclear review and a review loop command.

Workflow improving loop:
The one that improves the process itself, not just the output. A builder, a scorer, and a process optimizer agent that goes back over each iteration and suggests ways to make the whole claude code workflow better.

Whether you're deep into ai coding or just trying to figure out what are claude loops and what claude code agent loops can do, this covers the full picture, from a basic loop to a self-improving one. This is loop engineering the way we run it every day.

All of these agents, commands, and skills are available in AI Labs Pro, so you can download them and use them in your own claude code workflow. If loops, agentic systems, and getting more out of Claude Code is your thing, subscribe and hit the hype button.

00:00 Intro
00:38 Loop engineering
01:43 The stateless loop
03:50 Sponsor
04:53 The learning loop
07:22 Multi-agent review loop
09:27 The verification loop
11:35 The workflow improvement loop

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#ai #claude #claudeCode #claudeAi #aiAutomation #anthropic #vibeCoding #claudeLoops