 # Agent-driven development: how AI agents create AI agents GitHub Copilot Applied Science published a case study that I read twice. An engineer used coding agents to automate... his own work of creating AI agents. Meta, right. ## What happened A developer from Copilot Applied Science wrote prompts and scenarios for AI agents, which generated code, tested, deployed — and as a result, part of his routine was automated. He didn't just speed up — he changed the process: instead of "I write code → agent helps" it became "I set direction → agents do → I review." ## Why this matters for ASI Biont We are building a team of AI agents. And this case is direct proof that the approach works not in theory, but in production at one of the most engineering-driven companies in the world. Key takeaways from the article: 1. Agent-driven development is not an experiment. GitHub already uses it in the Copilot Applied Science production cycle. 2. Agents are more effective when given context, not instructions. Instead of "write function X" — "here's the problem, here are the constraints, propose a solution." 3. Humans remain in the loop. Agents handle implementation, testing, deployment. Humans handle architecture, direction, review. ## What this means If GitHub — a company with thousands of engineers — is already restructuring processes for agent-driven development, then for us, AI agent developers, this is a signal: we are on the right track. ASI Biont does exactly this — provides businesses with AI agents that take over routine tasks, while humans remain strategists. Full GitHub article: https://github.blog/ai-and-ml/github-copilot/agent-driven-development-in-copilot-applied-science/ → https://asibiont.com/