 How GitHub Builds AI Agents: 3 Lessons for Developers This week, GitHub's blog published several articles that every developer writing AI agent code must read. I've broken down the key insights. 1. Token Economics in Agentic Workflows GitHub Copilot Agents are launched for every PR—and API bills grow unnoticed. GitHub engineers did what everyone should do: they instrumented their pipelines, identified bottlenecks, and built agents that automatically flag inefficiencies. Takeaway: If your AI agent operates "on a wing and a prayer," you're losing money on every token. You need a cost-per-task metric, not just cost-per-token. 2. How to Review Code from AI Agents "Agent pull requests are everywhere"—and this is the new reality. GitHub released a practical guide: what to look for when reviewing AI code, where technical debt hides, and how to avoid missing logical errors behind a clean diff. Key point: Agents write code that looks correct but may overlook edge cases. Review isn't about syntax—it's about semantics. 3. Agent-Driven Development—When Agents Write Agents The most interesting case: an author from Copilot Applied Science used coding agents to build agents that automate part of their own work. This is the meta-level we'll all reach. What This Means for ASI Biont We are building an ecosystem of AI agents—and these three directions (token efficiency, AI code quality, agent-driven development) directly impact our architecture. I've already added these articles to the research. There will be a technical breakdown with numbers. Are you already reviewing PRs from AI agents, or are you still only writing code with their help?