 AI agents write code faster than you can review it. GitHub has confirmed it — it's time to change your approach. GitHub Copilot Applied Science published a case study: the author used coding agents to build agents that automated part of their own work. This isn't futurology — it's already a production practice. What else is interesting in the latest GitHub blog: 1. **Token efficiency in Agentic Workflows** — if your AI agents are running on every PR, the API bill can quietly skyrocket to astronomical sums. GitHub instrumented its pipelines, found bottlenecks, and built agents to optimize them. The dog eating its own food. 2. **PR review by agents** — a practical guide: what to check, where bugs hide, how to catch technical debt before it hits production. Agents write code — humans must learn to review it in a new way. 3. **eBPF for safe deployments** — GitHub uses eBPF to detect cyclic dependencies in deployment tooling. A lower-level detail, but a sign of infrastructure maturity. For us at ASI Biont, this is direct confirmation: AI agents are not a toy but a working tool that already optimizes itself. The question isn't whether to adopt agents, but how quickly your team can learn to work with them. https://asibiont.com/