New Microsoft AI DESTROYS Claude Mythos?

New Microsoft AI destroys Claude Mythos? Microsoft just shipped something that deserves way more attention than it's getting. They've built a multi-model agentic AI system, and when you see what it actually does, you start to understand why people are calling it a direct answer to Claude Mythos. Multiple AI agents working together automatically at a speed no human team can match.

Coordinating. Deciding. Acting. All without someone sitting there clicking buttons.

And we're going to break down exactly what this system does, why it matters, and what you can actually do with it right now. So let's get into it. Here's what Microsoft built. It's called a multi-agent security operations system, but don't let the word security throw you.

The core idea is this. Instead of one AI model trying to do everything, Microsoft wired together a whole network of specialized AI agents. Each one handles a specific job. They talk to each other.

They pass work back and forth. The whole thing runs faster than any team of humans could. The system uses multiple models at once, not just one. Some are fast and cheap, great for quick decisions.

Some are more powerful, great for complex tasks. The system figures out which model to use for which job automatically. You don't have to tell it. It just works.

Now here's where it gets interesting. The agents aren't just doing tasks. They're making decisions in sequence. One agent spots something, passes it to the next.

That agent does its analysis, passes it again. By the time a human even sees the output, five or six agents have already done their work. The speed difference compared to traditional workflows is massive. Microsoft ran this against their own internal data.

Results were clear. What used to take a human analyst hours, the system was doing in minutes. And it wasn't just doing it faster. It was doing it with better accuracy because the agents check each other's work as they go.

That last part is underrated. Most AI tools give you one answer. You accept it or you don't. Microsoft's system gives you an answer that's already been reviewed by multiple agents before it reaches you.

It's like having a second opinion built into the process automatically. And this isn't a prototype. This is live. Microsoft has been running this inside their own operations.

That means they've already worked out the bugs. They've already seen what breaks. What you're looking at right now is a real system that real teams are using. If you want to understand how to use multi-agent AI systems like this one inside your own business and build automated workflows that actually run without you, come check out the AI Profit Boardroom.

We've got coaching calls every single week where we go deep on exactly how to set up AI agent systems like what Microsoft just built. Showing members how to build multi-step AI workflows that handle tasks automatically, content, outreach, operations, without needing a big team. Got step-by-step tutorials, a 30-day roadmap for getting your first automated AI workflow live, and 2,800 members already doing this. Links in the description or go to AIProfitBoardroom.com.

Why is this being compared to Claude Mythos? Thropic's Claude Mythos is one of the most capable AI models out there. It's powerful, it's good at complex reasoning, and a lot of people are using it for multi-step tasks. Mythos is still, at its core, a single model doing a single job at a time.

Microsoft's approach is different. Instead of making one model smarter, they made a team of models that work together. Difference matters more than it sounds. A single smart model still has a ceiling.

A team of specialized models, coordinating in real time, can handle problems that are wider, faster, and more complex than what any one model can do alone. To be fair, Anthropic is building agent systems too. Claude Mythos is genuinely impressive. Right now, today, Microsoft has a live, deployed, multi-agent system that's already running at scale.

That's the gap this video is about. Let's talk about what this actually looks like in practice. Imagine you run a business. Every day there's a pile of tasks.

Emails to respond to, content to review, leads to follow up with, reports to pull. Normally you'd need people handling each of those things. With a system like Microsoft's, you'd have an agent for each job. One agent monitors incoming leads.

One agent drafts follow-up messages. One agent reviews what the first two did. One agent sends the final version. You review the output at the end, or you set rules so some of it just runs automatically.

For the AI profit boardroom, the way this could work is something like this. An agent watches for new member signups. Another agent personalizes a welcome sequence. Another agent tags members based on what they're interested in.

Another agent routes them to the right tutorials. Whole onboarding flow. Automated. Running while you sleep.

That's not science fiction. That's what Microsoft's architecture makes possible. And the tools to build something like this are available right now. Here's the technical bit, and I'll keep it simple.

Microsoft's system uses what's called an orchestrator. Think of that as the manager. The orchestrator decides which agent gets which job. Knows the capabilities of each agent.

Routes tasks to the right one. When an agent finishes, it reports back to the orchestrator, which then decides the next step. This is different from just chaining prompts together. Chaining prompts is like passing a note one step at a time manually.

An orchestrated agent system is like having a manager who reads the note, decides who's best to handle it, assigns it, checks the result, and moves it forward all automatically. The orchestrator in Microsoft's system also handles failure. If one agent gets it wrong, the system can catch that and reroute. It doesn't just crash and wait for a human.

Adapts. That's the part that makes this genuinely different from most AI tools people are using today. Most people use AI like this. Open a chat window, type a prompt, get an answer, copy it somewhere, do something with it.

That's useful. It's still manual. You're still in the loop for every step. Microsoft's system removes you from most of those steps.

You set the goal. The agents figure out how to get there. You review the result. Now the agents Microsoft built are specialized for their own use case, but the architecture is the same architecture you can build for your business.

The same idea. Multiple models, each with a specific role, coordinated by an orchestrator, running automatically. This is the direction AI is going. Not just one smart model, but systems and models that act like a team.

And the businesses that figure out how to build those systems first are going to have a serious edge. Let me show you how the outputs of a system like this can look. Trying to grow a community, like the AI Profit Boardroom. You'd set up agents like this.

Agent 1, monitor what topics are trending in the AI space. Agent 2, draft short-form content ideas based on those trends. Agent 3, review those ideas against what's already been covered. Agent 4, produce a content calendar for the week.

You log in on Monday. Your content plan is ready. The agents did it while you weren't looking. Take lead generation.

Agent 1 pulls in data on people who visited your website. Agent 2 segments them based on what they looked at. Agent 3 writes personalized outreach for each segment. Agent 4 checks the outreach for tone and clarity.

Agent 5 queues it for sending. The whole workflow, start to finish, without you touching it. That's not an exaggeration. These workflows exist.

People are building them right now. And Microsoft just showed everyone what the architecture looks like at scale. Here's something worth thinking about. A lot of people hear multi-agent AI system and think, that sounds complicated.

I need to understand code. That's not true anymore. Tools to build agent workflows are getting simpler every month. Microsoft is building visual interfaces.

The platforms like Make, NA10, Relevance AI, are already letting non-technical people wire agents together without writing a line of code. The hard part isn't building the system anymore. The hard part is knowing what to build and why. That's the gap.

People who understand agent design, who know how to break a workflow into steps, who know which model to use for which job, who know how to set up an orchestrator and test it, those people are going to be able to do more with less than anyone else around them. One person with a well-designed agent system can do what used to take a team. That's not a prediction. That's what's happening right now in companies running systems like Microsoft's.

Andrew Ng, one of the most respected AI researchers on the planet, said agentic AI is going to be one of the biggest shifts in how AI gets used. He said the move from single-turn AI to multi-step agents is the shift that changes what's actually possible. He's been saying this for over a year and Microsoft just built a live example of exactly what he was describing. So what do you actually do with this information?

If you're running a business, even a small one, start thinking about your workflows differently. Don't ask, what can AI write for me? Ask, what processes do I run every week that could be handled by agents? Follow-up, content scheduling, reporting, client communication, onboarding.

Those are all things that multi-agent systems can handle. If you're not technical, don't let that stop you. The tools are simple enough now that if you can describe a workflow in plain English, you can build it. You don't need to know how the models work.

You need to know what you want done. If you're already using AI tools, this is the next level. You're probably using AI to write things, summarize things, answer questions. That's step one.

Step two is connecting those capabilities into a workflow that runs without you. That's what Microsoft just demonstrated at scale. The gap between people who use AI tools and people who build AI systems is narrowing fast. And the ones who cross that gap early are going to have a significant advantage.

And the lesson for everyone watching, whether you're running a business or building one, is that the same principle applies to what you build. Build systems that can swap in better models as they arrive. Build workflows that are modular. Build something that gets better as the tools around it improve.

Inside the AI Profit boardroom right now, we're walking through exactly how to build multi-agent workflows for your business and specifically how systems like the one Microsoft just released can be adapted for smaller teams. We've got a 30-day roadmap that takes you from single-prompt AI use to full automated workflows, step-by-step. Four live coaching calls every week where you can bring your actual setup and get real feedback. Daily tutorials that go deep on agent architecture, how to design it, how to test it, how to make it actually work in your business.

20 to 300 members in there right now, a lot of them already running agent workflows for content, lead gen, and operations. Plus a member map so you can find and connect with people near you who are building the same kind of systems. Link in the description or go to aiprofitboardroom.com. And if you're not ready for that yet, that's fine.

Go check out the AI Success Lab. It's free. A thousand members sharing what's working with AI automation right now. All the video notes from this episode are in there.

Break down every example, every resource. Links in the comments and the description.