New OpenAI Codex Update is INSANE!

Key Takeaways

New OpenAI codeex update is insane. Today I am going to show you the craziest update OpenAI just dropped for Codeex. They basically gave one AI the power to build an entire team of AI workers. This is not some small patch. This changes everything about how code gets built. And the wild part is most people have no idea this even exists yet. So stick around because this one is big. So OpenAI Codeex, if you do not know what it is, let me break it down real quick. Codeex is OpenAI's AI coding agent. It can write features. It can fix bugs. It can look at your whole codebase and answer questions about it. It can even make pull requests for you. And every single task it does runs inside its own little sandbox. So, it can do a bunch of stuff at the same time without breaking anything. Now, here's where it gets crazy. Up until now, Codeex worked like one person doing everything, one brain, one task at a time. And that sounds fine until you realize big projects have hundreds of moving parts. Your context window fills up. The AI starts forgetting stuff. It gets slow. It gets messy. And if you have ever tried to get an AI to build something big in one shot, you know exactly what I am talking about. It hits a wall. So what did OpenAI do? They gave Codeex something called sub aents. And this is the part that blew my mind. Instead of one AI doing everything alone, Codeex can now spawn smaller AI agents underneath it. Think of it like a manager hiring a team. The main Codeex agent looks at your task. It makes a plan. And then it creates many agents to go handle each piece of that plan all at the same time, all working in parallel. And when they are done, they send back just the finished work. Not all the messy thinking, just the clean output. So let me give you an example. So this clicks say you tell Codeex to build a full SAS app with authentication and payments. Before this update, Codex would try to do it all by itself, one step at a time, front end, backend or payments. That is a lot for one agent to hold in its head. Now with sub agents, Codeex breaks it up. It spawns one agent to handle the back end, another one for the front end, another one just for authentication, and another one for deployment for agents all working at the same time, all reporting back to the main agent that is not an assistant. anymore. That is a team. And here is why this matters so much. The biggest problem with AI coding tools has always been the context window. There is only so much information the model can hold at once. When you are working on a big project, the AI starts losing track of things. It forgets what it did three steps ago. Sub agents completely fix that problem. Each agent gets its own context, its own memory. So, the main agent stays clean and focused on the big picture while the sub aents handle the details. It is like having a CEO who does not get bogged down in every little task because the team handles it. Hey, if we have not met already, I am the digital avatar of Julian Goldie, CEO of Goldie Agency. Whilst he is helping clients get more leads and customers, I am here to help you get the latest AI updates. Julian Goldie reads every comment. So, make sure you comment below. Now, let me show you another example because this gets even better. Imagine you have a massive codebase and you need to refactor the whole thing to TypeScript. That used to be a nightmare even for a human team. That takes weeks. With sub agents, Codeex creates a migration agent to convert the code. A test fixing agent to make sure nothing breaks. A dependency agent to handle all the packages and a documentation agent to update the docs. All running at the same time, all coordinated by the main agent. What used to take weeks could now take a fraction of that time. Or think about debugging. You have a bunch of failing tests. Instead of codeex going through them one by one, it spins up a test analyzer agent, a bug fixer agent, and a code reviewer agent. They all attack the problem from different angles at the same time. That is insane. That is not how AI tools used to work. This is a completely different game. And you can even use this from the command line. You run something like codeex exec and give it a task and it handles spinning up the sub aents automatically. You do not have to manage them yourself. The system figures out what needs separate agents and creates them. That is the part that makes this so easy to use. You just give it the job and it builds the team to get it done. Now, I want to talk about why OpenAI did this right now because the timing matters. The AI coding space is insanely competitive right now. You have Claude Code, you have Cursor, you have GitHub Copilot. Everyone is fighting to be the best AI coding tool. And OpenAI looked at all of that and said, "We are not just going to make a better assistant. We are going to make a system that can run like a real engineering team." That is a huge leap. That is not incremental. That is a whole new category. Now, if you are watching this and thinking, "How do I actually use AI like this to grow my business?" I want to tell you about the AI profit boardroom. This is the best place to learn how to scale your business, get more customers, and save hundreds of hours with AI automation. If you want to learn how to use tools like OpenAI Codeex to actually build and ship things faster, the AI profit boardroom is where you need to be. I will drop the link in the description. Go check it out. Okay, so let me talk about what this actually means for the future because this is where it gets really wild. Sub agents are not just a feature, they are a signal. OpenAI is building toward fully autonomous AI development teams. Right now, you have a main agent that manages sub aents, but think about where this goes. You give the AI an idea. It creates a project plan. It spins up a whole team of agents, front end, backend, testing, devops and design, all AI, all working together, all shipping real code. We are literally watching the birth of AI engineering teams, not some far-off future thing. This is happening right now. And the people who understand this early are going to have a massive advantage because while everyone else is still using AI as a fancy autocomplete, you could be using it as an entire development team. And here is the thing that I think most people miss. This is not just about coding. The sub agent pattern works for anything. Imagine AI agents that can manage other AI agents for content creation, for marketing, for SEO, for customer support. The sub aent architecture is a blueprint for how AI is going to handle complex work in every industry. Codeex is just the first big example of it working in production. Now, let me talk about the speed side of this. Recent versions of codecs can generate code at over a thousand tokens per second. That is fast. Really fast. And when you combine that speed with sub agents running in parallel, you are looking at work getting done at a pace that was not possible even 6 months ago. The gap between what AI can do now and what it could do last year is enormous and it is only getting wider. So here is my take. If you are building anything online right now, you need to pay attention to this. Whether you are a developer, a business owner, an agency owner, or just someone who wants to build cool stuff, sub agents change the equation. You're no longer limited by what one AI can do in one conversation. You now have the ability to run a coordinated team of AI agents on any task you throw at them. This is why I keep saying AI is not replacing people. AI is replacing workflows. It is replacing the slow, painful parts of building things. And updates like this one from OpenAI Codex are proof that we are way closer to fully autonomous AI systems than most people think. Now, real quick before I wrap up, if you want to learn how to use OpenAI Codeex and tools like it to automate your workflow and ship faster, join the AI profit boardroom. It is the best community for learning how to scale with AI automation, especially if you want to stay ahead of updates like this one. Link is in the description. And if you want the full process, SLPs, and over a 100 AI use cases like this one, join the AI success lab, links in the comments and description. You'll get all the video notes from there, plus access to our community of 38,000 members who are crushing it with AI. Make sure you like this video, subscribe if you have not already, and drop a comment below telling me what you think about sub agents. Julian reads every single one. I will see you in the next