OpenAI Codex Agent in Linear is INSANE! : Automate ANYTHING!

What if you could assign bugs to an AI teammate and get a pull request in minutes? Linear just dropped Codeex integration and it's absolutely wild. I'm going to show you exactly how to use it. This is going to change how your team ships code. Hey, if we haven't met already, I'm the digital avatar of Julian Goldie, CEO of SEO agency Goldie Agency. Whilst he's helping clients get more leads and customers, I'm here to help you get the latest AI updates. Julian Goldie reads every comment, so make sure you comment below. Today I'm showing you something that sounds like science fiction, but it's real. You can now assign bugs and issues to an AI agent in Linear and it will write code, run tests, and create pull requests for you. No joke, this is OpenAI codeex integrated directly into Linear. And it's one of the most powerful things I've seen this year. Here's what most people don't know. Linear now lets you tag an AI agent called Codeex. Just like you tag a teammate, you type a codeex in an issue, and boom, it spins up a whole coding environment in the cloud, reads your repo, figures out what needs to be done, and starts working. And here's the crazy part. It posts updates back to Linear, so you can watch it work in real time. Let me break down exactly how this works because there are two ways to use it, and you need to know both. The first way is Codeex Cloud. This is the fast automated way. You connect your GitHub to Codeexcloud, install Codeex in your linear workspace, and then whenever you have a bug or task, you just mention a codeex in the issue. Codeex reads the issue, picks the right repo and branch, spins up a sandbox environment, and starts coding. It can read files, edit code, run tests, and when it's done, it creates a pull request for you to review. The second way is codec cli and IDE extension. This is for power users who want to work locally. You install the codeex CLI on your machine and connect it to linear using something called model context protocol or MCP. This lets codeex pull in context from your linear issues while working on your local code. So if you're already in your editor or terminal, Codeex can fetch the issue details, understand what you're trying to fix, and help you build the solution right there locally. Now, stay with me because I'm going to show you both of these in action, and you're going to see why this is such a big deal. But first, let me tell you why this matters for your business or your team. Think about all the boring, repetitive tasks your developers do. Triaging bugs, writing first drafts of fixes, running tests, creating pull requests, updating documentation. All of that takes time. Time that could be spent building new features or solving harder problems. Codeex handles the boring stuff. It's like having an extra junior developer who works 24/7 and never complains. And because it runs in a sandbox, it's safe. It can't break your production environment. Here's what I want you to remember. Codeex is not replacing developers. It's making them faster. It's handling the tasks that nobody wants to do so your team can focus on the work that actually moves the needle. And by the end of this video, you're going to know exactly how to set this up for your team. All right, let me show you the first demo. This is Codeex Cloud in action. I'm in linear right now and I have a bug report here. Now, watch what happens when I mention it at Codeex in a comment. I type at codeex and linear shows me the autocomplete. I click it and hit send. Within seconds, codeex starts posting updates to the issue and it says working on this and drops a link to the codeex cloud task. Now, here's where it gets interesting. I click that link and it takes me to codeexcloud where I can see exactly what the agent is doing. It's loaded the entire repo into a sandbox. It's reading the login component code. It's checking the mobile styles. It's running tests to see what broke. And now watch this. Codeex finds the issue. There's a CSS media query or that's overriding the button click event on mobile. Codeex rewrites the code, runs the tests again, and they pass. Then it generates a pull request. All of this happened in about 2 minutes. Think about how long it would take a human developer to triage this, find the issue, write the fix, test it, and create a PR. Learn probably 30 minutes minimum. Codeex did it in two. Now, here's what you need to pay attention to. You still have to review the code. Codeex is not perfect. It's really good, but it's not a replacement for human review. You need to look at the PR, test it yourself, and make sure it actually fixes the problem without breaking anything else. But even with that review step, you're saving massive amounts of time. All right. Now, let me show you the second way to use this. This is the codec flow for local development. This is more powerful if you're already working in your terminal or your IDE and you want codeex to help you with a specific linear issue. First, you need to install the codeex cli. You run codeex login and sign in with your chat GPT account. Then you run codeex connect linear to link your linear workspace. Now when you have a linear issue you want to work on, you can tell codeex to fetch that context using MCP. Now let me talk about the technology behind this because it's actually fascinating. Codeex uses something called model context protocol or MCP. This is a

standard that lets AI agents securely access context from third party tools like Linear, GitHub, documentation, and more. When you connect codeex to Linear through MCP, you're giving it permission to read your issues, but only the ones you specify. It can't access anything else unless you explicitly allow it. The cloud version runs each task in its own isolated sandbox. That sandbox is preloaded with a snapshot of your repository. Codeex can read files, modify them, run commands, and execute tests, but it's completely isolated from your production environment. Nothing Codeex does in the sandbox affects your live code until you review and merge the pull request. If you're watching this and thinking, I want to automate more of my business with AI tools like Codeex, you need to check out AI Profit Boardroom. This is where we go deep on AI automation strategies for agencies, SAS companies, and online businesses. You'll learn how to save time and automate your workflow with cutting edge AI tools exactly like the one I just showed you. Link is in the description. Now, back to the video. All right, so what should you actually use Codeex for? Let me give you some real world use cases. First, bug triage. When bugs come in, you can assign them to Codeex for a first pass. Codeex will analyze the bug, try to reproduce it, and often fix it automatically. Even if it doesn't fix it, it'll give your developers a head start with context and analysis. Second, test-driven development. If you write tests first, you can give those tests to codeex and let it write the implementation. This works really well for straightforward features where the requirements are clear. Third, codebase Q& A. You can ask codeex questions about your codebase. Where is the authentication logic? How does the payment flow work? Codeex can read through your entire repo and give you answers with file references. Fourth, documentation. You can ask Codeex to write documentation for functions, APIs, or entire features. It reads the code and generates docs that actually make sense. But here are the limits you need to know. Codeex is not a replacement for code review. You always need to review what it builds. It can make mistakes. It can misunderstand requirements. it can introduce bugs. Always test locally before you merge. Also, be careful with sensitive repos. If you're working with customer data, payment systems, or proprietary algorithms, think twice before giving codeex access. Use the local CLI flow instead of cloud if you're worried about data privacy. And always use lease privilege. Only give Codeex access to the repos and permissions it actually needs. This technology is moving so fast. 6 months ago, this didn't exist. Now you can have an AI agent writing code for you in production environments and it's only going to get better. The teams that adopt this early are going to have a massive advantage. They're going to ship faster, fix bugs faster, and free up their developers to work on harder problems. If you want to stay ahead with AI tools like this, you need to join AI Profit Boardroom. We cover every major AI release, break down exactly how to use it, and show you how to apply it to your business. Learn how to save time and automate your business with AI tools that actually work. All right, that's it for today. Go try Codeex in linear. Tag me in the comments and let me know what you build. And if you got value from this, hit the like button and subscribe because I'm dropping AI updates like this every single week. See you in the next one.