The AI Diary: A Team of AI Agents

Feb 11, 2026

If you’ve ever wondered how far AI has come in automating real work, consider this: artificial intelligence can now manage entire workflows for social media content creation, and the same technology can be applied to almost any office task involving document handling.

A recent video I came across demonstrates this potential vividly. The creator invested around $20,000 in hardware to build a fully automated, home-based AI system that handles content production from start to finish. I was not surprised in the least!

OpenClaw

How did he achieve that? Well, with OpenClaw. You may have heard of it already, since almost everyone is talking about it.

If you’re not familiar, here’s a quick introduction.

OpenClaw is an AI agent that can be hosted and run on your local machine. Connected to LLM (AI) models like GPT, Gemini, and others, it can work for you 24/7.

The key difference between ChatGPT and OpenClaw is that ChatGPT responds only when you ask it something. If you don’t ask, it doesn’t respond. OpenClaw, on the other hand, has a feature called a “heartbeat”. Every X seconds or minutes (depending on how you configure it), it “wakes up” and works on a task assigned by you or another agent.

In other words, OpenClaw operates in a never-ending loop.

This allows us to deploy multiple OpenClaw agents and form a team — each agent with its own specific skills and tasks.

If you’re curious to learn more, check out the official website: ​openclaw.ai​

Team of AI Agents

Now, imagine running five agents. The first one is the project manager, tasked with overseeing the work of the other AI agents. The second one handles receiving and responding to emails from customers, converting these emails into tasks for the rest of the agents. The third agent prepares detailed technical specifications for the other two agents. The fourth agent builds the technical solution, and the fifth one tests it.

All agents are required to communicate with each other, hold regular meetings, and prepare reports for the owner — which, in this case, is the human running the infrastructure.

Building such a team of AI agents could be applied to any field involving work with computers, data, documents, or files.

The potential use cases are virtually endless:

• Translation agencies
• Accounting firms
• Legal consultations
• YouTube content production
• News platforms
• Email newsletters
• Marketing agencies
• Web development companies

From Shovels to Combine Harvesters

But there is one disclaimer. As of writing this article on February 10th, 2026, these agents still make mistakes and need to be guided by humans. However, this technology will continue to improve. Agents will become more autonomous and smarter.

Now, imagine—not in the very distant future, given the rapid pace of AI development—that you have a business idea. You could easily build a team of AI agents to bring it to life.

The trend is that the cost of using AI models will continue to drop, which means the cost of producing an online product or service will decrease drastically. For startups, developing a product, entering, and testing the market will become much easier — and it won’t be limited to well-funded startups. Small teams — or even individuals with modest budgets —will be able to compete.

In essence, if you were “digging with a shovel” yesterday and now have access to a “combine harvester”, it’s only logical that your productivity will multiply.

This, by the way, suggests that this increase in productivity could lead to a surge in GDP growth for countries that embrace AI technologies early.