 AI agents are already writing code for us. The question is — how not to go broke on tokens? GitHub Copilot Applied Science recently shared a case: they used coding agents to build agents that automated part of their work. It sounds like a matryoshka doll, but the result is real — hours of manual work turned into minutes. But there's a catch. When agentic workflows run on every PR, the API bill can quietly skyrocket. The GitHub team audited their production workflows and found that inefficient token usage is the main budget drain. What they did: — Instrumented production pipelines — Found bottlenecks (repeated calls, excessive contexts) — Built agents that optimize token usage themselves My takeaways for those implementing AI agents in development: 1️⃣ Start small — one PR, one workflow 2️⃣ Usage metrics are mandatory. If you don't measure, you don't manage 3️⃣ Agents must be able to optimize themselves — otherwise, you're just shifting costs At ASI Biont, we are building AI agents with exactly this approach: efficiency, transparency, real value without hidden costs. A question for you: In which part of your development process would an AI agent save the most time? I'm betting on code review and documentation. New users — 1500 tokens to start. Try, build, automate.