This NEW Chinese AI Agent is INSANE! (FREE + Open Source!)

This new Chinese AI agent is insane, and it's completely free and open source. Alibaba just dropped Quen Agent World today, June 24th, 2026, and this thing is genuinely different from anything else out there right now. Here's the short version. Most AI agents learn by doing things in the real world.

They open a real browser. They run real searches. They touch real files. That works, but it's slow, expensive, and hard to scale.

Quen Agent World does something different. It builds a virtual world inside the AI itself. The agent practices inside that world first. It simulates what would happen before it ever touches anything real.

Think of it like a flight simulator. Pilots don't learn on real planes first. They practice in a simulator. They crash 100 times.

They learn what works. Then they fly the real thing. Quen Agent World does exactly that, but for AI agents, and here's why that matters. The biggest AI agent on the planet right now is GPT 5.4.

Quen-AgentWorld-397B scores 58.71 on Agent World bench. GPT 5.4 scores 58.25. The open source Chinese model just beat it. Claude Opus 4.8 and Gemini 3.1 Pro are both behind it too.

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. The 35 billion parameter version, Quen-AgentWorld-35B-A3B is free, open source, available on Hugging Face right now.

It already shows an 8.66 point improvement over its base model just from world model training. That number matters because most model improvements are measured in fractions of a percent. 8.66 points is enormous. What does it actually do?

It covers seven environments, all inside one single model, MCP, which is how AI tools talk to each other. Search, simulating what Google results would look like. Terminal A, command line stuff, software engineering, web browsing, operating systems, and Android, mobile apps, seven environments, one model, and it handles all of them simultaneously. Here's what's wild about how it was trained.

Alibaba didn't use fake data. They set up real physical servers, real virtual machines running Ubuntu and Mac OS and Android, real browsers. They ran AI agents on those machines continuously and recorded every single interaction, over 10 million real world interactions across all seven domains. Then they trained the model on that.

So when Quen-AgentWorld simulates a search result, it's not guessing. It's drawing from 10 million real examples of what search results actually look like when an agent uses them. The training happened in three stages. First, CPT, continual pre-training.

The model learned how environments actually work, how they behave, what a terminal session looks like, what a web page structure looks like. Then SFT, supervised fine tuning, where it learned to predict what comes next. Then reinforcement learning, where it got rewarded for simulating environments accurately. The most interesting finding from their research, factuality was the hardest part.

The model improved 11.3% on factual accuracy, but it was still the lowest scoring dimension throughout, which means even the best AI world model in existence struggles to get facts right inside a simulation. That's a real honest limitation they published openly. Now let's talk about why this actually matters for people growing a business with AI. Inside the AI Profit Boardroom, we teach AI automation.

We help business owners use tools like this to save time and get more customers. And Quen-AgentWorld opens up a genuinely new category of automation. Building reliable AI agents is hard. Agents fail.

They click the wrong thing. They misread a page. They break. The reason is usually that they weren't trained well enough on the environment they're working in.

Quen-AgentWorld is trying to fix that at the foundation level. Better trained agents means more reliable automation. More reliable automation means you can actually hand tasks off and trust they'll get done. If you want to know how to build AI automations like this for growing the AI Profit Boardroom, finding leads, creating content, following up with members, we have a 30-day roadmap specifically built around AI agent workflows.

We run four coaching calls every week where we go deep on tools like Quen, how to set up agent systems that run without you, and how to get more customers from AI automation over business owners inside right now. A lot of them already running agent workflows. Link in the description or go to AIProfitBoardroom.com. Back to Quen-AgentWorld.

Let me walk you through how these seven environments actually work in practice because this is where it gets interesting. MCP, Model Context Protocol. This is how AI tools talk to each other. Think of it like a universal adapter.

Your AI can call other tools, get back results, take action. Quen-AgentWorld can simulate what those tool responses look like before they happen. So an AI agent can plan its moves based on what it expects to get back. Search simulation.

An AI agent doesn't need to actually run a search to see what comes back. The model predicts the results. For the AI Profit Boardroom, that means an agent could plan a full content strategy knowing what search results will look like without touching Google once during planning. Terminal.

Command line. If you've ever watched a developer type into a black screen, that's the terminal. Quen-AgentWorld can simulate those interactions. An agent can plan a whole sequence of commands and check what would happen at each step before executing anything.

Software engineering. The SWE domain means Quen-AgentWorld can simulate coding environments, code reviews, bug finding, test runs. The model can predict what happens when you run a piece of code before it actually runs. Web browsing.

This is the big one for most businesses. An agent that can simulate what a web page looks like, predict what a click will do, understand what's on a site before navigating it. That's powerful. Imagine an agent that researches your competitors, maps their pricing pages, and pulls back structured data.

All simulated before execution so it doesn't break halfway through. For growing the AI profit boardroom, you could have an agent simulate scraping competitor community pages, mapping what they offer, then generate a comparison document. One sentence prompt. Research the top five AI communities online and create a comparison showing why the AI profit boardroom is the best option for business owners.

The world model handles the planning. The agent handles the execution. OS, operating system, desktop automation, files, folders, applications. An agent that can navigate your desktop like a human assistant.

Quen Agent World can simulate those interactions before running them live. Android, mobile apps. This is new territory for most agent systems. Simulating what happens inside a mobile app before touching it, booking tools, CRM apps, anything on a phone.

If your business relies on mobile workflows, this opens up automation that wasn't possible before. Now here's the thing Alibaba also published. Agent World Bench. A brand new benchmark category built specifically to test world models.

Compare this to where AI agents were even six months ago. Agents were mostly single task. You'd build one agent to do one job. It would fail about 30% of the time.

You'd manually check its work constantly. Now we're looking at models trained on 10 million real world interactions covering seven different environments, predicting what will happen before acting. The reliability curve is moving fast. For business owners, the question isn't whether AI agents will be reliable enough to hand tasks off to.

The question is how quickly you learn to build and deploy them. The window to get ahead of this is real. Look, Quen Agent World just gave us a free open source world model that outperforms every closed model on agent simulation. Inside the AI Profit boardroom we're building out the exact playbooks for how to use agent systems like this for lead generation, content, community management, and client acquisition.

Four live coaching calls every week where we go deep on agent setup and automation workflows. Daily tutorials walking you through exactly how to implement tools like Quen in your business. A 30-day roadmap built around AI agent automation. A prompt library with templates for every workflow we just covered and members.

A lot of them already running agent automations to save time and get more customers. Member map so you can connect with people near you who are building the same thing. Link in the description or go to aiprofitboardroom.com. And if you want the full process, SOPs, and 100 plus AI use cases like this one for free, join the AI Success Lab.

Links in the comments and description. You'll get all the notes from this video plus access to our community of 67,000 members who are already building with AI. It's free. Join now.