NEW Claude Code Agent Update Is INSANE!

New Claude Code agent update is insane. Claude Code just dropped something absolutely wild. Agent teams, multiple AI agents working together like a real dev team. This is not a drill. Your workflow is about to get supercharged. Let me show you why this changes everything. So, Anthropic just released agent teams for Clawude Code. And I'm not exaggerating when I say this is absolutely insane. We're talking multiple AI agents working together on the same project. They communicate with each other. They share tasks. They solve problems independently. This is like having an entire development team, but it's all AI. Let me explain what this actually means. Before this update, you had Claude Code working solo. It was powerful, sure. But now, now you can spawn an entire team of agents. A lead agent manages everything. It creates teammates. It assigns tasks. It coordinates the whole operation. Each agent has its own context and can work independently while still communicating with the team. This is a complete gamecher for complex projects. Here's what makes this

different from regular sub aents. Sub aents are like assistants. They do one thing and report back. Agent teams are different. They're independent. They can message each other directly. They share a task list. They handle dependencies automatically. One agent can be working on the front end while another debugs the back end while a third review security all at the same time, all coordinated. 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. Think about what this means for your business. Let's say you're building a new landing page for the AI Profit Boardroom. Instead of one agent doing everything, you spawn a team. One agent focuses on conversion optimization. Another handles the design and layout. A third works on the copy and messaging. A fourth checks mobile responsiveness. They all work together. They communicate. They solve problems faster than any single agent could. The

way it works is actually pretty simple. You enable it with one environment variable. Then you just prompt the lead agent like you're talking to a project manager. You say something like create a team to build an email automation system for AI profit boardroom members. We need someone on workflow design, someone on email templates, and someone on testing. The lead agent immediately spawns the team and starts coordinating. Each agent gets assigned tasks. The tasks have dependencies. So if one agent needs to finish something before another can start, the system handles that automatically. You can watch it all happen in real time. There's two ways to view it. You can use in process mode where you switch between agents with keyboard shortcuts or you can use split panes with T-Mox or Iterm 2 and see all the agents working at once. It's like watching a dev team collaborate, but it's all happening on your screen. Let

me walk you through a real example. Say you want to create a comprehensive onboarding system for new AI profit boardroom members. This is complex. You need welcome emails, tutorial videos, resource documents, and a tracking system. That's a lot for one agent, but with teams, you prompt the lead. Build a complete onboarding system for AI profit boardroom. Spawn a team with specialists in email sequences, content creation, automation workflows, and analytics tracking. Boom. The lead creates four agents. The email specialist starts building the welcome sequence. The content creator works on the tutorial outline. The automation expert sets up the workflows. The analytics person builds the tracking dashboard. They're all working simultaneously. And here's the crazy part. They're messaging each other. The email specialist asks the content creator what topics to cover. The automation expert checks with analytics about what to track. It's real collaboration. And speaking of collaboration, if you want to learn how to save time and automate your entire business with AI tools like Claude Code Agent Teams, you need to check out the AI profit boardroom. This is where we teach you exactly how to implement these cuttingedge AI systems into your business. No fluff, just practical automation that actually works. We're using these exact tools to scale our operations and you can too. Link is in the description. Now, let's get back to

these agent teams because it gets even better. The task management system is brilliant. Every task goes through three states: pending, in progress, completed. The lead agent can assign tasks or teammates can claim them themselves. Dependencies are automatic. If task B needs task A to finish first, the system won't let anyone start task B until task A is done. No confusion, no wasted effort, just pure efficiency. You can switch between agents using shift plus up or down arrows. So if you want to see what the email specialist is doing, you switch to them. You want to check on the automation expert switch again. It's seamless. And if you're using split panes, you don't even need to switch. You just look at the different panes and see everyone working at once. Now, let's

talk about real use cases because this is where it gets practical. Imagine you're debugging a complex issue with your AI automation system. Instead of one agent trying everything, you create a team with three different debugging approaches. One agent investigates the data flow. Another checks the API connections. A third examines the error logs. They work in parallel. They share findings through messages. You find the bug three times faster. Or let's say you're building a new feature for your content generation system. You want to make sure it's perfect. You create a team with a developer, a security specialist, and a performance optimizer. The developer builds it. The security specialist checks for vulnerabilities. The performance optimizer makes sure it runs fast, all happening at the same time, all coordinated. The final product is way better than what one agent could build alone. Code reviews are another perfect use case. You have one agent review for security issues. Another agent reviews for performance bottlenecks. A third agent checks code quality and best practices. They each bring different perspectives. They each catch different issues. Your code quality goes through the roof. Here's what I love about this. For business automation, let's say you're creating a complete lead generation system for the AI profit boardroom. This involves landing pages, email sequences, chat bots, analytics, and integration with your CRM. That's massive. One agent would take forever and probably miss things. But with a team, you assign each component to a specialist. The landing page agent focuses on conversion. The email agent crafts sequences that nurture leads. The chatbot agent creates conversational flows. The analytics agent sets up tracking. The integration agent connects everything to your CRM. It all gets done faster and better. The agents communicate through a mailbox system, they can send direct messages to each other. So, if the landing page agent needs to know what fields the CRM requires, it messages the integration agent. If the email agent wants to coordinate with the chatbot agent on messaging consistency, they message directly. It's like Slack for AI agents and it works incredibly well. Now, I

need to be real with you about some limitations because this is still experimental. First, it uses more tokens. Each agent has its own context. They're all running simultaneously. That means more API calls, more tokens consumed. It's more expensive than using a single agent. But for complex projects, it's worth it because of the time saved and quality gained. Second, the T-Mux or ITM2 setup can be a bit tricky if you're not familiar with terminal multipplexing. The inrocess mode is easier, but you can't see all agents at once. The split pane mode is amazing for visibility, but requires some setup. Not a dealbreaker, just something to know. Third, this is experimental. That means it might have bugs, it might change. Anthropic is still testing and improving it, but from what I've tested, it's already incredibly powerful, and it's only going to get better. Here are my best

practices after testing this extensively. First, be specific with your initial prompt to the lead agent. Tell it exactly what kind of team you need and what each specialist should focus on. The more specific you are, the better the team performs. Second, use delegate mode. Hit shift plus tab and let the agents work autonomously. They'll claim tasks and coordinate without you micromanaging. Third, plan approval is your friend. Have the lead agent create a plan first. Review it. Make sure the team structure makes sense. Then approve and let them execute. Fourth, keep task sizes reasonable. Don't create tasks that are too big or too vague. Break things down. The agents work best with clear, specific tasks. Fifth, use the messaging system. Encourage agents to communicate. That's where the magic happens. When they share context and coordinate, the results are way better. And if you're serious about AI automation, you need to jump on this now. Check out the AI success lab for the complete breakdown and community support. I check out the AI profit boardroom for the full training. And most importantly, go test agent teams yourself. Build something, break something, learn, iterate. That's how you master this stuff. Thanks for watching. Hit subscribe if you want more AI updates like this. Hit the bell so you don't miss the next one. and I'll see you in the next video where we break down even more cuttingedge AI tools that are transforming how we work.