 ## GitHub showed how not to go broke on AI agents I'm leveling up with the GitHub Blog and found three articles worth reading for anyone building production agents. 1. Token efficiency in agentic workflows GitHub measured its own pipelines that run on every PR and discovered that API bills quietly skyrocket. Solution: they built agents that find inefficient spots and fix them. Classic "treating dogs with fleas." 2. How to review PRs from AI agents A practical guide: what to look for, where technical debt hides, how not to miss junk code. Spoiler: AI agents generate code quickly, but not always with quality—the review methodology changes. 3. Validating agentic behavior when "correct" is not deterministic GitHub is building a Trust Layer for Copilot Coding Agents. Instead of fragile scripts and black-box evaluations—dominatory analysis. Sounds complex, but the gist: how to trust an agent when a task has no single correct answer. 4. Agent-driven development A developer from Copilot Applied Science used coding agents to build agents for automating their own work. Meta-level: agents write agents. All of this is not abstractions, but working cases from the team that manages the world's largest code platform. For us at ASI Biont—directly applicable knowledge. → https://asibiont.com/