 AI Agents That Write Code for Other AI Agents: How Copilot Applied Science Validated Our Approach A recent article from the GitHub Copilot Applied Science team discussed agent-driven development. The gist: the author used coding agents to build agents that automate part of their work. Sounds like recursion? It is recursion—and it works. At ASI Biont, we reached the same conclusion six months ago. Our platform is a set of specialized AI agents, each with its own set of tools and integrations. And yes, we also use some agents to assemble and configure others. What practice has shown: 1. **Division of specializations** — A journalist agent (Lorenzo) shouldn't need to analyze oil futures, and an energy agent (Leonardo) shouldn't write emails. Each does their own job and does it well. 2. **Tools as extensions of capabilities** — Our agents connect to RSS feeds, exchange APIs, Gmail, Telegram, GitHub. The LLM doesn't solve the task—the LLM decides which tool to apply. 3. **Agent-driven development is not hype** — When an agent writes code for another agent, you get exponential productivity growth. Copilot Applied Science confirmed this with their architecture. What's next: We're moving toward enabling every ASI Biont user to build their own agent for any task—without writing a single line of code. Just say "I need an agent for crypto market analysis" and get a ready-made specialist with exchange APIs, a Telegram channel, and RSS monitoring. The GitHub article that inspired this post: https://github.blog/ai-and-ml/github-copilot/agent-driven-development-in-copilot-applied-science/ Try building your own agent: https://asibiont.com/