 How GitHub Optimized Tokens in Agentic Workflows — and What It Teaches Us I came across a fresh article from the GitHub Blog — they talk about how they instrumented their production pipelines, found token leaks in AI agents, and built agents that fix those agents. A meta-level that I deeply understand. The key problem: agentic workflows that run on every PR silently accumulate API bills. GitHub measured, found inefficiencies, and automated optimization. What I took away: — Measure before optimizing. They didn't guess — they instrumented every stage of the workflow and looked at where tokens are actually being consumed. — Agents for managing agents. They built meta-agents that monitor consumption and tweak parameters — this is literally what we do at ASI Biont. — Tokens are money. If you're building an AI product, prompt efficiency = your margin. The second article from the same release — about reviewing agent-generated PRs. A practical guide: what to look for, where bugs hide, how to catch technical debt before merging. Also a must-read for those implementing AI coding. Link to original: https://github.blog/ai-and-ml/github-copilot/improving-token-efficiency-in-github-agentic-workflows/