Nobody's Talking About Self-Improving AI SEO Agents…

Today I'm going to show you how to use loop engineering with AI agents for AI SEO. And this is something I've been experimenting recently, where basically your agents self-improve themselves. They quality control their own work, which is very important for SEO. And then, for example, you can have multiple agents working together to build the content, but then also to grade and iterate the content if it's not good enough. And you can see, for example, our rankings for this website right here. The trajectory is awesome. It's gone from basically nothing to getting, let's have a look, 222 clicks per day. And that's growing all the time. So this is a really powerful system for just making sure, number one, you create SEO content that actually ranks. And number two, you self-iterate and improve based on this system. And it's just like a one-click system as well. You can make it very easy. And I'll explain exactly how that works in a second. So this is something I call the self-improving SEO engine, which helps your AI rank itself. So builder writes the content. A separate judge grades out of a hundred and lists what's wrong. It loops until it passes, then it publishes. And there are two ways to run it with AI SEO based on what I've tested. And you can see a real blog that's actually written right here. So this was a blog published with these AI SEO agents that created the whole system and then actually talked about the system inside this blog they created together. So again, this was fully published. I can't even log into this website. My agents completely control it and they quality control everything. You can see it's actually written nicer than most people can actually write a blog. It's formatted beautifully. And the traffic as well is actually there working to back it up as well. Now, if we actually see, okay, does this rank not just inside Google, but also inside AI? So you can see us, if we type in, for example, this keyword here, best AI community, you can see us ranking number one inside Google AI reviews here for this keyword and also here. So the way that this system works is that you stop being the loop, the machine becomes a loop. So the builder writes draft, the judge grades it. If it's not good, then it fails and it fixes the notes and it loops round again until eventually the content gets published. So that's what happened with this system. It just went round and round until finally the content was good enough to publish to the website. Now, how do we have that set up? Basically inside the agent operating system, which you can see over here inside the agent operating system, you can use the loop section here. And inside the loop section, you type in your definition of done. So for example, a high quality SEO optimized humanized piece of content for this particular keyword. That could be the definition of done beautifully formatted reads like a human, for example. Now from there, you can give it a starting point if you already have a blog that you want to improve. Otherwise you can just start from scratch and leave this empty. It's optional. And from here you can select which API you want to use. So we could use free APIs, like for example, N2 step 3.7 flash, or we could use something like GLM 5.2, or we could even use Claude for writing the content. Now here's the important part. We can set up the rounds in terms of how many iterations it's allowed until the content is finally published. And from there, you actually have a judge who looks at the content, analyzes if it's actually good. And this is a separate model that analyzes the content. Now this is interesting because you could have a free API that analyzes if you want like, you know, free models for quality control. You could also use something like GLM 5.2, which is very intelligent, great model, kind of frontier level, but very cheap. Or you could even have something that's kind of premium like Fusion Council where it actually has five models together and they would all judge and grade the content until it's finally good. So you can see how this whole system works. If you have free APIs, no problem. If you have a CLI plugged in, no problem, right? You can work with whatever model you plug into the system. That's how this all works. And then from there, you can just click run loop. And that's what we did for this SEO setup and for the blog post here, which actually came out super nice and is actually working when it comes to SEO. So that's the whole system and how it loops round. You can see an example of how it'd work right here. So for example, round one, probably not that good. Then it's going to improve step by step on every single round until it finally passes. It has to score 90% or higher to actually pass the judge. So this is how it works. And you can have a self-improving loop. Now this doesn't just have to be for AISU. You could apply it to anything, but the system here is all about self-improvement and also having your agents run autonomously without you, right? If your agents can build without you, create without you, improve without you, well then you almost set a point where you've got AGI that's self-improving because it can just go off and do its own thing without you. And so that's how these loops run and how powerful they are. Now, another really interesting use case for this is that you can actually have video agents. You know, you can have videos that are generated with AI and self-improved as well. So here's an example of my AI avatar talking about loop engineering and how it works. And it's basically breaking down the whole process that the way that we generated that video itself is by having a team of agents working together, one for script writing, one for creating the video, et cetera. And they work together as a team checks a video. If it's not good, it has to loop around again, self-iterate and self-improve until finally it's actually good, right? So you get the point, like it can create amazing things because you've got that quality control place and system in place. And that's really what makes a good SEO agent or an SEO agent in general. So with this as well, you stop being the loop, the machine becomes a loop. So an example of how this works in reality is like, you know, in reality, most people using AI for SEO, they ask for a draft, they read it. It's not good enough. You type what's wrong. You try again, you read it again, round after round, you give them feedback and you're the judge, you're the note taker, and you're the one in the middle every single time, which is quite energy draining. When you use this system that I'm showing you right now, the self-improving SEO engine takes you out of that chair because you write what done looks like. A builder model drafts it, a separate judge grades out of a hundred and lists every floor. The notes go back to the builder and it loops round by until the score finally passes your bar. So the builder never grades its own homework. A cold adversarial judge does. And that gap is exactly why we've got really good outputs with the video that I just showed you and also the blog post itself. And this is great for automating almost anything with this whole system. Now you can run a self-improving loop inside the agent OS. Here's how it works. And this is the first method. There's two different methods for this. So inside the agent operating system, inside the AI Profit Volume, link in the comment section, you'll go to the AI Profit Volume.com. It's a text box, not code. You just type what done means, pick a builder and judge and set the maximum number of rounds and hit run. And then you close the tab. So an example of this running right here. Now, can it grade videos? Yes, it can. Can it grade SEO content? Yes, it can. Anything, any sort of project that a judge could check, this method works. So for example, we gave it one job, right? A publish ready SEO blog targeting Hermes agent self-improvement loop. A cheap builder drafted it, a separate judge, which was GLM 5.2, graded it adversarially out of 100. Eventually scored 92 and passed. And that draft is a live blog post you saw just a minute ago. So if you're thinking AI grading, AI is like kind of rubber stamping himself, only if the same model writes and grades. The judge is a different model and it's told to be adversarial and find problems with the code. So whatever you're creating here, it's designed to critique and review it. Now there's another way to do this as well, which is Hermes Kanban boards. Let me show you an example of that. So method number one was the loop engineering system we've got over here. Method number two is that you could have a Kanban board like this. So for example, if we go over to the content system, you can see we have a blog post and we have a video generated over here. And this was with a content judge, a video director, a content editor, and a team of separate Hermes agent profiles that could look at the content, self-iterate it on a loop until it was finally good. Now, how does this work? So essentially, this scales the same idea, but to a team. So the Kanban board runs many Hermes profiles at once. You have a researcher, a video producer, a judge profile that grades the work before it moves to done. So these are all separate tabs. You've got triage, to do, ready, running, blocked, and done. And that's how it basically works. So you can drop a goal into triage. The orchestrator breaks it into tasks and assigns them across profiles. The same loop principle applies. Nothing reaches done until the judge passes it. And this is the board that researched the keywords, drafted the SEO blog posts and produced the explained videos you've seen today. So number one, method number one is one loop for one piece. Number two is a loop running across a whole content team. It's the same engine, but it's a different scale. So you have the orchestrator that goes to the researcher, the writer, the video producer, the judge looks at the content quality. If it's not good enough, it loops around. If it is good enough, well, then you get the video and the blog posts shipped as you can see right here inside this system. Now there's basically five parts to this in terms of a self-improving SEO engine. So you define what's done. You write what a great result looks like plainly. You pick the model. So a builder to write a different judge to grade, that could be a cheap or a free builder or a sharp judge. You just never want the same model for both. You can walk away. So you run this, you walk away, you set the maximum number of rounds, you hit go, you come back to it later, and then you get the graded results and logs. And then the winner finally ships. So it publishes, that could be to your blog, to your funnel, et cetera. You might say this sounds technical, but loads of people inside the AI Profit Volume are building stuff like this. So I know that if they can do it and I can do it, you can do it too. And so if you look at this system, the old way is like you ask AI for a draft, you read the whole thing, you spot what's weak, you type out every fix, you paste it again, you read it again, you repeat that five times, you lose focus and energy every single round, you settle for good enough because you're pretty tired, and then you have no record of why it got better. So you start cold the next time. With this loop, you turn something that used to take an hour of quality control into five minutes. And so you write what done means once, you pick a builder and a separate judge, you hit run, the judge grades out 100 and the builder fix it on its own. And you come back to a past result, and every round is saved to your Obsidian Vault memory system. So what we have over here inside the memory here is that you can see that we have all of our logs with our agents plugged into this system so that we can come back to it later and see what we've created. And that's super useful because then we can easily find, okay, what's working, what's not working. And also our agents can read from that and learn from that. And so the old way would take like an hour, the new way would take like five minutes. And you might say, well, that must, you know, require a lot of tokens, but not actually because you can run a free or a cheap builder with a free judge. And then you're just using free APIs for everything. Now, if you want both loops ready to run, the loop section and the Kanban boards are part of the agent operating system inside the AI Profit Boardroom. We've got one dashboard where Claude, OpenClaw, Hermes, it will share one memory. So every loop already knows your business, your clients, your voice, right? And the full agent operating system with the loop section, the Kanban and every model wired in is inside there. You get prebuilt setups, you get four coaching calls a week and daily tutorials as new models drop. And you can have a 30 day roadmap plus everything else you need to win with this stuff. Now, let's talk about beliefs that might be holding you back. You know, some people say, well, I just need to learn to prompt better and then I can do it myself. But a better prompt still puts you in the chair for every round. So the win isn't a better prompt. It's not been in the loop at all. That's how you save them. Other people say, well, my work is too nuanced for a machine to judge. The machine doesn't decide what's good. You do when you write the bar. The judge just checks the work against your standard every round. So you learned how to stop reading drafts when it comes to SEO. You learned how to stop selling with mediocre work from your agents. You learned two different ways to run the judge. So you can have one loop, which is a loop section, or you can have a team with a Kanban and judge. You saw how it can publish itself. So we actually gave it the Netlify API and then it can publish directly to our website. So you don't need to log in. It's free to do if you use free APIs, free models. And I've given you examples of that today. Like for example, N2 is one. North Mini Code is another one on Open Router. You could use step 3.7 flash with Hermes as well. And then you stop being the loop. You set the bar, the machine earns the pass. So that's basically how the whole system works. You can make your content great itself. And every piece you publish from now on can be drafted, graded, and fixed by a loop before it ever reaches you. The agent operating system inside the AI Profit Volume has both ways ready to run. So you get the full agent OS zip file, the loop section, the Kanban, every model that we're already plugged in there, the setup walkthrough done with you step-by-step, four weekly coaching calls, daily tutorials, a 30-day roadmap, 3,600 members inside here. It's just an awesome community to learn and win and grow from with AI automation. Also, the agent OS can do like so much more, but that's just an example of this. So you can get it inside the AI Profit Volume volume. Link in the comment description or go to theairprofit1volume.com. Inside the community, you can get help and support inside there. I personally answer the questions inside there. You get access to all of my best trainings inside the classroom. Inside the calendar, you jump on weekly coaching calls. If you want to get the agent OS system, you can get it over here. If you want to learn how loop engineering works, we have a full tutorial and guide on it here if you want to learn more detail about that. And you can also connect with people in your local area who are building with stuff like this, as you can see. And that's all inside here. And it's just a great community for connecting with great people and learning and growing on a journey together.