I Ran 100 AI Agents for 10 Minutes — Here’s What Happened
admin I typed one command. One single prompt. And suddenly, 100 AI agents lit up my screen and started working simultaneously. One was writing code, another was generating content, a third was researching, and a fourth was running tests. I didn’t lift a finger. I leaned back in my chair and watched.
In this article, I’ll show you exactly what those 100 agents produced in 10 minutes — and why the result made me rethink everything I knew about running an online business.
Key Takeaways
- One command launches 100+ parallel AI agents that split a goal into subtasks and work simultaneously
- My four core agents — Cloud (decision brain), Antigravity (content/visuals), Xcodex (development), and Hermes (learning coordinator) — handle research, coding, content, and self-improvement
- A single AI-cloned video reached nearly 1 million views and generated over $73,000 in affiliate revenue within roughly a year — without paid ads
- API costs start from $5 on Open Router, with free tiers available on several tools
- This isn’t magic — it’s system design: the agents learn from mistakes and improve over time
Why I Built This: The Burnout That Broke Me
Let me be honest. Doing this alone is impossible. You need to create content, write code, do research, send emails, track sales, and respond to customers. There are 24 hours in a day, and you’re one person.
I lived this for years. I’d sit at my computer at 6 a.m., look up, and it would be midnight. Why? Because I was doing everything myself.
Then something changed. AI stopped being just a tool you ask questions to. It became something that could actually do the work for you — and not just one instance, but hundreds running at the same time.
What Makes AI Agents Different From Chatbots
Here’s the critical distinction. With a normal AI, you ask a question, get an answer, and that’s it. An AI agent system works completely differently.
You give it a goal. It takes that goal, breaks it into hundreds of pieces, and creates a separate agent for each piece. One does this, another does that, all at the same time.
I call this my “DJ crew.” They don’t ask for salaries, don’t take breaks, don’t get sick. They work at 3 a.m. if I need them to.
When I type a goal — something like “build this system for me” and hit enter — within seconds, dozens of agents activate in the background. Each one runs to a different task. One creates a plan, one writes code, one tests it, one fixes errors. I’m not moving a finger. I’m just watching.
Work that used to eat hours of my day now happens in parallel right in front of me. And no, this isn’t magic. It’s a properly built system.
My Four-Core Agent Stack: How the System Actually Works
The agents don’t just work in isolation. They’re connected across different tools and software, talking to each other. Whichever one is best for a specific domain takes over that task.
Cloud: The Decision Brain
Cloud is the brain behind my operations. The other agents consult it to make the right decisions. With Cloud’s approval, they proceed to execution. I run this on the Opus 4.8 model in the DIN framework — currently the most capable model I can access. I set it to “Effort Max” for the best results, though I should warn you: at the “Deep Note” level, it burns through serious token counts.
Antigravity: Content and Visual Command Center
Antigravity handles all the backend coordination for my content creation — the code, research, and key outputs that need to be visualized and managed so all agents can collaborate effectively. You can use it free up to a certain limit; just download it to your computer. In my setup, I run the DIN model with Gemini 3.5 High Flash, which has given me the best results for content generation. It uses Nano Banana technology for visual production, and the output quality is excellent.
Xcodex: Development and Design Powerhouse
Xcodex handles the development side, particularly UGC and frontend design. It produces professional website designs at a level I couldn’t have imagined before. This is where the visual execution happens.
Hermes: The Learning Coordinator
Hermes is what ties everything together. It connects all the other agents, ensures they learn from their mistakes continuously, and drives their self-improvement. These four agents together monitor everything happening in the world — news, trends, any data point you can think of — checking it in seconds, then executing, developing, researching, learning from errors, and pushing everything to the next level.
The $73,000 Proof: What 100 Agents Actually Built
“Okay, but what do you actually do with this?” That’s the fair question.
I build these systems for large companies, automating work that used to cost them tens of thousands of pounds per month in salaries. After paying me serious money for the setup, these systems work 24/7 for them — never missing a client, constantly improving operations.
But let me show you something more concrete. I told my AI: “Create a video for me.”
The AI cloned me and produced a video on my behalf. It edited it, added text, wrote the description — everything. That video landed on a platform where tens of thousands of people commented. It reached nearly 1 million views on the live channel.
“But Akın, I can record a 5-minute video myself,” you might say. True. But here’s what makes this different: this system learns from its mistakes by analyzing human brain responses. It studies how our brains react to specific colors, patterns, and speech structures — the same way social media platforms keep you watching. My AI analyzes predicted human interaction patterns from the script before ever publishing, then releases the content.
The result? Thousands, tens of thousands of people reached. And here’s the revenue side: I wasn’t even running ads on this. Looking at the roughly one-year period since publication, this single video generated over $73,000. The AI handles responses to commenters too — identifying interested users and sending them links automatically.
Another agent handles distribution across hundreds of social platforms, scheduling at optimal times, researching hashtags, analyzing performance, and learning from errors to improve reach.
How to Build This: The Setup Process
Let me walk you through how to actually build this.
Step 1: Get the foundation. I provide a complete PDF with all materials, links, and my scoring system for the best skills and tools — Open Cloud, Cursor, Codex, Gemini, GitHub, and more. This isn’t just about Hermes; it’s a complete toolkit.
Step 2: Install Antigravity. Download it to your computer. It’s free to use and handles operations fast. You literally just tell it: “Hello, install this for me from this link.” It creates files, asks for permissions (I recommend approving all), and builds your agent in about 3-5 minutes.
Step 3: Fuel your agents with the right models. Think of it this way: you have a car, but do you have gas? The models are your fuel. I use Open Router, where you can get API access for very low costs — sometimes nearly free depending on the model. New users need to load about $5 to access paid models.
Create your API key, set time or spending limits if you want, and you’re connected. My agents use multiple LLMs depending on the task — DeepSeek V4 for certain operations, Gemini Flash for chat, others for specific functions. The key insight: no single model is best at everything. The agents dynamically select whichever model delivers the best results for each specific task.
What This Actually Costs to Run
Let’s be realistic about expenses. You can run Ollama locally for free if you have a powerful computer (about 10GB space needed), but in my experience, it doesn’t run smart enough for serious work. The browser-based approach through Open Router starts at $5.
Model rankings shift constantly. Opus went from 4.7 to 4.8. GPT-5.5 performs well in agent systems. DeepSeek V4 delivers incredible value at very low cost. I’ve checked nearly 50 different LLM ranking dashboards, and they often show very different results — so take any single leaderboard with caution.
What matters is your actual output quality per dollar spent. DeepSeek and Gemini Flash currently lead on cost-efficiency for most agent tasks, while Opus 4.8 ranks #1 on trend metrics despite being 15th on some static rankings. The calculation includes both success rate and cost.
From Theory to Revenue: My Real Numbers
I don’t just talk about this — I live it. My agent system currently works with over 4,000 companies in the background, generating revenue daily. One of my current targets is £100,000 monthly; this month, the system produced approximately $47,200.
I’ve built roughly 15 computer systems in the background that all communicate with each other, cross-referencing and self-correcting. They operate like the world’s best AI experts — one agent might analyze like Elon Musk on marketing, another like Steve Jobs on product, another like Warren Buffett on finance, tracking every token spent and every dollar of revenue. Underneath, 200+ sub-agents each handle specific micro-tasks.
I’ve also applied this to e-commerce: one agent finds products on eBay, researches them, and automatically loads them into my store. I just wait for the sales notifications — hundreds of orders coming in regularly.
FAQ
Do I need coding skills to set up AI agents?
No. I don’t have advanced technical knowledge myself, yet I built my entire agent system using AI. Tools like Antigravity handle the installation automatically — you just paste a link and approve permissions. The PDF guide I provide includes step-by-step instructions with all necessary links.
How much does it cost to start running AI agents?
The minimum viable start is about $5 on Open Router to access paid models. Several tools offer free tiers: Antigravity is free to a certain limit, and Ollama can run locally at no cost if you have sufficient computer power (approximately 10GB storage). My complete setup uses multiple models, but you can begin with a single API key and scale gradually.
Can AI agents really generate income automatically?
My system has generated over $73,000 from a single AI-produced video and approximately $47,200 in recent monthly revenue across operations. However, this required building proper systems, testing, and continuous refinement. The agents learn from errors and improve over time — but they need correct initial setup and ongoing oversight. I never promise guaranteed income; results depend on implementation quality and market conditions.
What’s the difference between AI agents and regular AI chatbots?
A standard chatbot responds to individual prompts with single answers. An AI agent system receives a goal, autonomously breaks it into subtasks, creates multiple specialized workers, and executes them in parallel — including self-correction, research, and multi-tool coordination. My 100-agent setup runs across different software platforms simultaneously, with each agent using whichever AI model performs best for its specific task.
Final Thoughts: The System Matters More Than the Hype
I’ve been in this space since May 2023, when I launched Turkey’s first AI training course. Over 3,000 people enrolled within the first day. I’ve since created hundreds of thousands of pieces of content and trained countless people to build their own businesses.
What I’ve learned: the people who succeed aren’t the ones chasing every new AI tool. They’re the ones who build systems. One well-designed agent with the right models, skills, and feedback loops outperforms ten thousand tools tested randomly.
Start with the foundation. Connect your agents properly. Choose your models based on actual task performance, not hype. And most importantly — build toward revenue, not just impressive demos. The technology is real. The results are real. But only if you treat it as system design, not magic.
Watch the full video (in Turkish — English subtitles available):
Tools & Community
- TurkoLister — the AI listing tool I use to turn Amazon products into optimized eBay UK listings in about 60 seconds (from £4.99/month, £1 one-week trial).
- AI & E-commerce Community — my Turkish-speaking community ($19/month) with weekly live sessions.
- Subscribe on YouTube — new experiments every week.
