Big Idea

The argument in one line.

Agent loops are not a new capability but a disciplined way to chain Ralph loops together, and the only reason they burn tokens is that most people skip the three controls every production loop needs.

Who This Is For

Read if. Skip if.

READ IF YOU ARE…

You use Claude Code or Codex daily and want the actual signal behind the write-loops-not-prompts discourse.
You have run parallel agent sessions manually and want to know when automating that pattern is the right move.
You have hit unexpected token costs with agentic workflows and want a repeatable framework for preventing them.
You are comfortable with the /goal command and want to understand how Workflows fit into the picture.

SKIP IF…

You have never run a Claude Code or Codex session end-to-end — this assumes hands-on agentic experience.
You want deep engineering implementation of multi-agent orchestration systems at scale.

TL;DR

The full version, fast.

Every agent loop is a series of Ralph loops — fresh-context, goal-directed cycles — chained together with hard guardrails. The three controls that prevent runaway costs are a max iteration cap, stall detection, and a per-sub-agent token ceiling. Three loops worth running now: issue backlog drain via /goal, UI verification (screenshot, DOM inspect, compare against design specs, retry), and a code review babysitter that loops on a timer to auto-rebase and shepherd PRs. The prerequisite for all of them is already being comfortable running 2-3 parallel agent sessions manually without losing the thread.

Free for members

Chat with this breakdown — free.

Sign in and you get 23 free chat messages on us — ask for the hook, quote a framework, find the exact transcript moment, generate a markdown action plan. Bring your own key when you want unlimited.

Create a free account →

Chapters

Where the time goes.

00:00 – 00:44

01 · Hook / Intro

Opens on Peter Steinberger's viral tweet. Frames the video as finding the middle ground between AI haters and AI defenders.

00:44 – 04:29

02 · How Did We Get Here?

Lineage walkthrough: ReAct paper (reason-act-observe) -> AutoGPT (self-prompting, rabbit-hole prone) -> Ralph Loop (forced amnesia, fresh context each iteration) -> /goal (Ralph loop with completion criteria).

04:29 – 05:47

03 · What Are Loops?

Defines a loop as a small program that prompts, reads, decides done/not-done, re-prompts. Argues context quality and guardrail granularity are personal decisions, not universal defaults.

05:47 – 09:51

04 · Controls and Token Budgets

The three controls: max iterations, no-progress detection, token ceilings. Uber's AI budget disaster as a cautionary case. Workflows vs pure loops — why external orchestration logic is safer.

09:51 – 14:54

05 · Questions Still Remaining

Three honest open problems: handling ambiguity (vision.md as partial answer), where planning lives (intent vs implementation), and where the human needs to step back in. Prerequisite check: can you run 2-3 parallel manual sessions today?

14:54 – 20:21

06 · The 3 Loops You Need

Loop 1: issue backlog drain via /goal on GitHub issues. Loop 2: UI verification loop (screenshot, DOM inspect, compare against design specs, retry to pixel fidelity). Loop 3: /babysit code review loop from Boris Cherny.

Atomic Insights

Lines worth screenshotting.

A loop is just a small program that prompts the agent, reads what it produced, decides if the task is done, and re-prompts if not.
AutoGPT failed because it let the agent self-prompt without a progress check and would go down rabbit holes indefinitely.
The Ralph loop solved AutoGPT's problem with forced amnesia: a fresh conversation each iteration so the model approaches the problem with fresh eyes.
/goal is a Ralph loop where the agent knows when it is done based on your stated criteria instead of a fixed iteration cap.
Agent loops did not become dangerous — they became popular. The token horror stories are misconfiguration, not an inherent flaw.
Workflows are safer than pure agent loops because the orchestration logic lives in a JavaScript function outside the agent, not inside the agent itself.
The three controls every loop needs: max iterations cap, no-progress detection, and a token ceiling per sub-agent.
Uber blew their entire AI budget in four months and capped engineers at $1,500 per tool per month — without controls, this is the default outcome.
Loops are plumbing. Skills are what you pour through the pipes. A loop without strong skills is just automated confusion.
If you cannot run 2-3 parallel manual agent sessions confidently today, building a loop is a bad idea right now.
A vision.md file documenting the core problem, intended solution, key goals, technical principles, and success criteria is the closest standard answer for how loops handle ambiguity.
The question of where the human needs to step back in is the most personal and most important question in loop design — there is no universal answer.
The best loops being built today integrate existing skills and custom slash commands rather than inventing new agent capabilities.
A UI verification loop can compare screenshots against design specs and retry automatically until pixel fidelity meets a defined threshold.
Boris Cherny's /babysit loop runs every 5 minutes, auto-addresses code review comments, auto-rebases, and shepherds PRs to production — all from a single slash command.

Takeaway

Three controls that keep agent loops from running wild.

WHAT TO LEARN

Loops do not inherently waste tokens — they waste tokens when you skip the guardrails, and the guardrails are three specific settings, not a vibe.

Every agent loop is a chain of Ralph loops: fresh context each iteration, a goal the agent checks itself against, and a hard cap for when it gets stuck.
The three controls every loop needs before running in production are a max iteration ceiling, a stall detector that aborts when no progress is made, and a token limit per sub-agent.
Moving orchestration logic into an external function such as a Claude Workflow prevents the agent from overriding its own guardrails mid-run.
A vision.md file documenting the core problem, intended solution, key goals, technical principles, and what success looks like is the closest thing to a standard answer for how loops handle ambiguity.
The prerequisite for building a loop is already being able to run 2-3 parallel manual agent sessions and be happy with what they produce unaided.
Loops amplify the skills you already have; if the underlying skills are weak, a loop automates bad outputs faster rather than fixing them.
The three lowest-barrier loops worth running now are the issue backlog drain via /goal, a UI verification loop that retries until pixel fidelity meets your design specs, and a /babysit code review loop on a timer.

Glossary

Terms worth knowing.

ReAct: A 2022-2023 research pattern (not the JavaScript library) in which a model Reasons about what to do, Acts using a tool, observes the result, and repeats the cycle until done.
Ralph Loop: An agent pattern that runs the same prompt in a fresh conversation on each iteration, enforcing a maximum number of tries. The fresh context prevents compounding context drift.
/goal: A built-in command in Claude Code and Codex that runs a Ralph-style loop but stops when the agent determines the stated goal is complete rather than when a fixed iteration count is hit.
Workflows (Claude): A Claude Code feature that moves orchestration logic into an external JavaScript function, removing the agent as the orchestrator of its own loop and reducing runaway behavior.
Token ceiling: A hard per-sub-agent cap on the total tokens it is allowed to consume in a single run. Set as a variable in Claude Workflows; acts like a financial circuit breaker.
Stall detection: A loop control that monitors whether the agent is making meaningful progress and aborts the run when the same error or dead-end recurs across iterations.
AutoGPT: An early 2023 experiment that gave a language model a goal and let it self-prompt toward completing it. Notable for creating sub-goals autonomously but prone to rabbit-holing and token waste.

Resources

Things they pointed at.

00:00linkPeter Steinberger tweet on loops ↗

00:44linkReAct paper (2022-23)

06:00productClaude Workflows ↗

11:50productvision.md pattern (Peter Steinberger)

16:14toolClaw Design

18:28toolBoris Cherny /babysit slash command

18:28toolOBRA superpowers (systematic debugging skill)

00:00productSean Kochel Skool community ↗

Quotables

Lines you could clip.

04:33

“A loop is a small program that you build that prompts the agent for you, reads what it produced, decides if it's done or not, and then re-prompts it if it's not.”

Self-contained definition, no setup needed→ TikTok hook↗ Tweet quote

14:22

“Loops are like plumbing. But if you're pouring concrete in eggshells down there, you're gonna have a really bad time.”

Vivid metaphor, lands hard on its own→ IG reel cold open↗ Tweet quote

13:50

“If you can't run that entire process yourself right now, building a loop is a really, really bad idea.”

Contrarian take, zero context needed→ TikTok hook↗ Tweet quote

07:00

“The orchestration logic actually lives outside of Claude Code itself.”

Short, punchy, counterintuitive for people who think Claude controls itself→ newsletter pull-quote↗ Tweet quote

The Script

Word for word.

Read-along

Don't just watch it. Burn it in.

See every word as it's spoken — crank it to 2× and still catch all of it. The same dual-channel trick behind Amazon's Kindle + Audible.

metaphoranalogystory

00:00One tweet and a video clip from OpenClaw in ClaudeCodes creator is apparently all it takes to make all of Twitter go absolutely nuts. You shouldn't be prompting coding agents anymore.

00:11You should be designing loops that prompt your agents. I don't prompt quad anymore. I have loops that are running.

00:18They're the ones that are prompting quad and kinda figuring out what to do. My job is to write loops. As usual, engineers that hate anything having to do with AI on one side and people that will defend anything having to do with AI on the other side.

00:31So in this video, we are going to try to suss out the middle ground. So we'll look at what these loops really are, how to avoid needing to take out a second mortgage just to run them, and I'll give you three simple loops that you can run this week. Starting with how we even got here in the first place.

00:47So I personally believe that if you wanna understand any of these arguments people are making, it's helpful to know the lineage of how we got to the conversation of having loops in the first place. So we'll spend two or three minutes here, and then we will get into loops and the examples of loops. So back in 2022 or 2023, there was this white paper, and it introduced this concept of React, not to be confused with the JavaScript library.

01:11And so this was, like, the first formalized version of an agent loop. So you would have a model that reasons about what to do.

01:18It uses tools in order to act on the thing. It reads the output of what it did, and it repeats until it's done. So one model, one loop, and a human watching it the entire time.

01:29So the benefit of this, models could start acting based on what the tools gave them instead of just hallucinating outputs all the time. So the next evolution of this stuff was something called auto GPT. And so this gave it a goal and basically asked the question, well, what if I let it prompt itself toward completing that goal?

01:46And so this was actually developed as a project from a video game developer. And so this thing could create sub goals, but it had problem of running out and going down rabbit holes. So it would waste a lot of time and a lot of tokens trying to do the stuff you wanted it to do.

02:02But the thing is, it showed that on some level with proper guardrails, this concept of autonomy could be possible. So then maybe, like, a year or two later, we get this concept of the Ralph loop.

02:15And so with the Ralph loop, it basically said, hey. I need you to run this same exact prompt over and over again until the work is actually complete or until you max out on the number of iterations that I'm gonna allow you to actually run. So instead of letting the history accumulate and clogging up all the pipes, we had this idea of, like, discipline through forced amnesia.

02:37Fresh conversation every single time this prompt ran, which meant it had fresh eyes, and it could try to approach the problem objectively. So this is where we started getting this concept of, like, a controlled loop is a super valuable thing to have. So then what happened recently is that both Codex and ClawdCode basically productize this idea of a Ralf loop, but with a slight change.

02:58And that change was instead of setting a maximum number of iterations, you can have it work until the underlying task and your intention for that task is actually complete. So a goal is basically a Ralph loop where instead of having a maximum number of iterations, it actually knows when it has completed the task based on the criteria you've set, which is a concept we will get to in a little bit because it's really important.

03:24So that takes us to where we are now in the object of a lot of people's apparent hatred for each other, which is this concept of an agent loop. And the general mental model here is that the creation of a loop to kinda harness everything that you are doing is more valuable than the individual prompts that you are sending through into the system.

03:47Now a lot of people claim to have really complicated involved versions of these things, but you can also set up agent loops that are actually, like, very simple to do, and a lot of people have already been doing this. But the thing is you've got loops on loops on loops, apparently controlling other loops. And so it's kind of like a multi agent orchestration version of a Ralph loop.

04:08And so with a lot of people complaining about token usage and wondering what loops even are in general, I don't really think at the end of the day that it's, like, super complicated of a topic. It's just it happens to be going viral right now, and so that's why everybody and their mother is probably about to start talking about it.

04:27So, like, what is a loop then? A loop is a small program that you build that prompts the agent for you, reads what it produced, decides if it's done or not, and then re prompts it if it's not.

04:41Now I'm not gonna sit here and pretend like doing that process is necessarily the easiest thing in the world, especially if you're trying to do really complicated things that a lot of engineers are probably doing. But the concept of authoring a loop that will do some sort of task for you and remove a lot of work from your plate is something that's super valuable, which we're gonna get to, and I'm gonna show you a few of these things in a minute or two.

05:04But I think the thing that's still really valuable no matter what type of loop it is that you are trying to create or run is that you start getting really detailed about the type of context that you have about you and your projects, the types of things that you wanna actually be in the loop about, and that you're giving really clear guidelines and guardrails so that the agents know when they can go off and make decisions by themselves and when they have to actually stop and check-in with you on things.

05:31And that is something, in my opinion, that's actually very personal to the person trying to run the loop, their skill level, their experience, the things that they actually want to be in control of, not just something that's like a a one size fits all that automatically applies to, like, every single person. So with all that out of the way, I wanna go through the three controls that you have to make sure this stuff doesn't fly off the rails and become one of those burning billions of tokens type of situations.

05:58So the reason that a lot of this is getting a lot of press is that Uber recently blew through their entire AI budget in four months. And so this concept of, like, reining in token costs and not just letting agents run and do duplicate work at infinity ad infinitum is a really important concept and topic right now, obviously.

06:17They have capped their engineers to, I think, $1,500 per tool per month that they're allowed to use. So as regular human beings that probably don't have a company budget paying for all this stuff, maybe we're just using, like, a codex CLI plan or a clawed max plan.

06:31Like, what do we actually practically do about it? So the first thing is that the mechanisms for this are a little bit different depending on, like, what exactly you're using, obviously. But the first thing that we have up is that goal command.

06:42And then the second thing that I think is actually, like, incredibly valuable if you do it the right way is Claude's new, uh, workflows. I'm not gonna go into depth on workflows because I did a whole video on that last week that you can go watch.

06:55I'll link to it at the end. But the reason that this is really valuable is that the orchestration logic actually lives outside of Claude Code itself.

07:03So one of, like, the the big issues with why a lot of this stuff used to go off the rails and not really work well and Claude Code or Codex would, like, lock into certain decisions that really didn't make sense and would just run with it is because the agent was responsible for the orchestration of the agent. And so one of the things about workflows that's really nice is that that's, like, externally managed by a JavaScript function.

07:30So that being said, what are the three controls? The first thing is setting a max number of iterations. So similar to, like, how the Ralph loops actually worked, even if we have, like, a goal in mind for what we are trying to do, we still wanna have hard caps in place in the scenario where, like, can't effectively actually reach that goal.

07:49We do not want it to just infinitely try to fix this thing and never stop, because it's not ever actually reaching the goal, because it's stuck somewhere. Now if you are using workflows as I showed in my my last video, you can actually set the number of max iterations because, again, you can control that inside of variables that are defined inside of the workflow.

08:12So that's, like, one really clear way to set guardrails for yourself so that these things don't fly off the rails and just explode with the number of tasks they're trying to run and agents that they're trying to call up. Number two is detecting when there's not progress being made, because this is, I think, where, like, runaway bills really tend to come from.

08:33When there's not progress being made in the task, and it's just constantly running into either the same issue over and over over again, or new issues keep cropping up, and now it's going down weird paths trying to solve the problem, and it's not actually really addressing the core root of what you want done. I think these, like, unbounded iterations and problem solving, again, is one of the reasons that you get these, like, huge explosions in token usage.

09:00And then number three, token ceilings. So you can think of these like a hard financial stop. So for example, when you are creating a clawed workflow, you can actually set the maximum number of tokens that different sub agents are actually allowed to use.

09:14So this is part of the reason why, like, I don't I I can understand where a lot of the the hate comes from because, hey. People gonna hate. Haters gonna hate.

09:22But if you're complaining about, like, blowing through tokens, that to me seems to be that, well, you're not configuring the thing properly, and you're just letting it run off and and do things that it shouldn't be doing. So we if we accept the idea that an agent loop is really you just automating a series of Ralph loops running together, and you're building them with intention, then it really shouldn't go out and start blowing tokens anymore than you doing all of those steps manually would have done anyway.

09:51In order for any of this to work, you have to have a really strong process for verifying and reviewing what has been done, which begs the question, against what standard exactly are you verifying and reviewing something?

10:05And to me, this is one of the areas that I think still really needs to be fleshed out. So here are the areas that I think there still really aren't super clean answers for, at least not for me. Number one is how do the loops actually handle ambiguity?

10:21So the thing is if you're not providing proper context to the model, it is going to have to make decisions on your behalf at game time. Now for certain types of decisions, you might not care about that, especially if you're not an engineer by background. But when it comes to, like, the business case for what you are building or how it's intended to solve problems for end users, in my opinion, that's something that, like, you need to either have, like, extremely well documented already in some sort of markdown file that these things are accessing, or don't use the loops unless you've gotten to that point where you're really clear on those things and you've found a way to pass that in and have the models actually handle that type of ambiguity in a way that you are happy with.

11:07So number two, where does the planning actually live in this agent loops type of system?

11:14Like, is your loop planning the intention behind something or just the implementation, or is it doing both?

11:21So now somebody, like, kind of asked this or, like, a similar question inside of this Twitter thread, and the answer from Peter Steinbega is that he uses a Vision dot markdown for all of his projects.

11:35Now he didn't answer, like, exactly what is in there for, but someone asked Grock. And according to Grock, it's the core problem, the intended solution, the key goals, the technical principles, and what success looks like. Now I think this as, like, an idea is super, super valuable, but this is something that, like, you as an individual have to test and see.

11:55Like, when you're prompting these things manually, does you having a vision doc, like, really start erasing a lot of the issues that you used to have? And, like, in a one on one session, you could trust that Codex or ClawdCode actually goes out and builds the thing and makes the type of decisions that you would have made in a situation where a decision needs to be made.

12:18Now point number three, which again, I'll I'll pull from this thread because I think a lot of people brought up a lot of the concerns that I had. Just like, where is the human in this process? Like, where does the human need to be?

12:29So for example, somebody says, like, well, you still kinda need to be in the loop to test stuff unless you literally have infinite tokens, which, hey, if you work at OpenAI or you work at Cloud Code, you probably do. I personally would love to see an internal job listing at all of these, like, AI model providers, where your job is to test all of their new features on an actual budget.

12:52So the third thing, which I think is the most critical piece of this entire process, and the answer is very dependent on you, your budget, uh, your knowledge of certain domains, whether or not you're an engineer, like, all of that type of stuff is where does the human need to actually step back in when we're talking about, like, a fully autonomous agent loop.

13:14I'm the author of agent loops and my own destiny. Like, that sounds well and good, but, like, realistically, where do you as a human being, unless you're an agent watching this, where do you need to actually step in and control that process and test things?

13:30So here's what I'll say. If you're not already comfortable running two to three, like, parallel sessions at the same time and managing all of those things and having agents go off and, like, actually build things that you are happy with purely from, like, the context you gave it and how you created that context, passed it between all the different agents you have.

13:52Like, if you can't run that entire process yourself right now, building a loop is a really, really bad idea. But if you are, I think the question simply becomes, like, how are you just going to automate this process better for yourself now? Because there's really two things.

14:07So we have our loops, which we have been discussing. But one thing that's really important in here for a loop to actually function properly are our skills.

14:17So the way that I think about it is that, like, loops are like plumbing. But if you're pouring concrete in eggshells down there, you're gonna have a really bad time. And so that's why, like, in this concept of even, like, authoring loops in the first place, I think skills are really the thing that make them effective.

14:34Like, there's nothing about a loop inherently that is some new capability that allows you to become, like, a super mega vibe coder, AI coder, AI engineer, like, by default because they have this convention called a loop now. Like, the best loops that I've seen and the best workflows that I've seen are integrating skills that already exist into how they work.

14:54So all that being said, I'm gonna go through three concrete loops that I think are really valuable for anybody to use, whether you're a beginner or even if you're more advanced than me at this stuff. These are at least three loops that I think anybody should or could be using.

15:08So number one is creating a loop for your issue backlog. So anytime I have a bug that I come across or an idea for a new feature or refinements that need to be made or even just something that I want to, like, remember for the future with a project and potentially come back to, I created as a GitHub issue.

15:27So here's an example of that. We could use the slash goal command, in this case, inside of Cloud Code, but they have this inside of Codex also. And you could say, I need you to go through any bugs listed in my GitHub issues and use systematic debugging skill from OBRA superpowers to debug them.

15:42Then you need to create a test case for that bug and commit it to my project. Once you're done each bug, open a PR. This is like a very simple example of a loop.

15:53Why is that? There are gonna be bugs in here. Some of them are gonna be simpler.

15:57Some of them are gonna be a little bit more involved, but we have the goal of those bugs being resolved. Now if you wanted to have, like, maximum number of iterations and other, like, cost control type things in this type of process, you can prompt that stuff in in natural language.

16:14So the second type of loop that I think is very valuable and also, like, pretty easy to get started with is front end verification. So one of the loops that I run pretty much anytime I'm doing, like, meaningful front end work is this UI verification loop. So let's say we make some sort of change on the front end.

16:33This could be, like, a new feature or making a modification to an existing thing that we have. Then we can kick off inside again of, like, Claude or inside of Codex or really anything. Can do this with, iOS simulators.

16:45You can kick off the Chrome extension to actually view the work that you have done, take snapshots of it, actually inspect, like, the the DOM under the hood, and then cross reference that against your specs and what your acceptance criteria and, like, verification criteria were for that feature. But the big thing that I love to use this for is actually cross referencing it against our designs.

17:11So if you use a tool like, uh, Claw Design, for example, you can literally have this same Chrome extension open up your designs inside of Claw Design and look at the two pieces of work side by side and continue to iterate and retry until the work that you've done locally actually matches the specs.

17:33And so this type of process, you could cap it on the number of tries that it's allowed to, like, actually go through. You could wrap it in a goal command so that it will continue going until everything has, like, near 100% fidelity to the actual designs that you had.

17:47There's there's a ton of different things that you can do there, but this is, like, a really valuable workflow that I think is pretty approachable to, like, anyone realistically. So if we were to pop back down, we can see this goal is now achieved. Now I only had one bug tagged in that repo, but if I had four, five, six, seven, eight, nine, 10 bugs, um, it would have continued moving through.

18:05We wouldn't have gotten this goal achieved notification until it actually fixed those the bugs across all 10 of those issues. So number three is code review loops.

18:15So, again, this one comes from Boris, uh, Cherny, which is the Cloud Code creator. And a lot of his loops are, like, process oriented things where he has systems that he would go through himself manually, but now he's just creating loops to actually run through these things.

18:33So for example, loop every five minutes. He has this custom slash command he called called babysit, and he wants to auto address code review, automatically rebase, and then shepherd his PRs to production.

18:46So the thing is when you start working with, number one, parallel agents, but number two, if you really start getting into loops, you're probably gonna be doing a lot more, and that means a lot more commits being made, a lot more pull requests. And the concept of a a loop is gonna help you actually, like, wrangle and maintain all of that.

19:04And so that's why setting these loops that are, like, checking on the progress of all of this volume that you're creating is really valuable, because if you don't have it, like, you're just gonna lose track of those things super quickly. But now, again, the thing to keep in mind is that he is using custom skills and slash commands for nearly all of these loops.

19:24So all that being said, I think goals and workflows are two really valuable ways of, like, doing the type of loops that people tend to talk about in a way where they're actually, like, very practical and, like, somewhat easy to actually, like, implement. That being said, you you really wanna be aware of best practices and, like, the patterns that these things can or should follow so that you don't accidentally end up creating a workflow where now you have hundreds of different sub agents on Opus 4.8 running and completely blowing through all of your budgets because that would suck.

19:59So, again, I did a video on some of those best practices last week that I'll, uh, I will link you guys to. So, yeah, if you like this video, check out the one I did last week on Claude workflows because it's kind of the manifestation in a lot of ways of this concept of a loop.

20:13And, of course, subscribe if you liked this video and wanna see more stuff like this. But that's it for this video. I will see you in the next one.

The Hook

The bait, then the rug-pull.

One viral tweet — "You should be designing loops that prompt your agents" — sent the AI-coding corner of Twitter into its usual civil war. Here is the calm read-through: what loops actually are, why the token horror stories are a configuration problem not an architecture problem, and three loops you can put to work this week.

Frameworks

Named ideas worth stealing.

00:44list

The Lineage Stack

ReAct (2022-23): reason-act-observe cycle
AutoGPT: self-prompting toward a goal, no guardrails
Ralph Loop: forced amnesia, fresh context, max iterations
/goal: Ralph Loop with completion criteria instead of iteration cap
Agent Loops: multiple Ralph loops orchestrated together

Historical progression showing each step solved the previous one's failure mode.

Steal forAny explainer that needs to contextualize where a new tool sits in an evolving landscape.

07:07list

The 3 Loop Controls

Max iterations cap
No-progress detection (stall abort)
Token ceiling per sub-agent

The minimum guardrails every production loop needs. All three are configurable in Claude Workflows as JavaScript variables.

Steal forAny checklist for reviewing a new agentic workflow before running it in production.

14:14concept

Loops vs Skills

Loops are plumbing infrastructure. Skills are the substance that flows through. A loop without strong underlying skills just automates bad outputs faster.

Steal forFraming the skill-building prerequisite in any onboarding guide for agentic AI workflows.

CTA Breakdown