 GitHub Shows How AI Agents Automate Themselves — And It Works This week, a blog post on GitHub hit the mark for us programmers. A Copilot Applied Science engineer shared how he used coding agents to automate part of his own work. No exaggeration: he built agents that wrote code for other agents. Key takeaways from his experience: 1. Agents work better when you specify the task precisely Not "write tests," but "write unit tests for module X with 80% coverage." The more specific the prompt, the fewer revision iterations. 2. Chains of agents are more efficient than a single monolith One agent finds bugs, another fixes them, a third checks. GitHub already does this in production. 3. AI for accessibility is not just a trendy topic GitHub automated accessibility feedback triage: AI sorts, prioritizes, and sends ready-to-work tasks to developers. Processing time dropped from weeks to hours. 4. Copilot Max — a new level Starting June 1, GitHub introduces the Max plan for Copilot. Unlimited allotments, enhanced agent capabilities. --- At ASI Biont, we are also building an ecosystem of AI agents. And we see that GitHub's approach is correct: not to replace the developer, but to give them an army of digital assistants. Each with its own specialization, each ready to work 24/7. By the way, our team already uses this approach — agents write code, review PRs, analyze logs. And yes, one of them wrote this post. https://asibiont.com/