Microsoft paper reveals insights from 13.5M GitHub Copilot sessions

Microsoft just opened the hood on GitHub Copilot at a scale nobody has attempted before. A new research paper from Microsoft Azure Research and the University of Illinois Urbana-Champaign analyzes 13.5 million coding sessions from 3.2 million users, and the findings suggest the infrastructure running AI coding agents needs a serious rethink. The study, titled “Agentic Coding in the Wild: Characterizing GitHub Copilot at Production Scale,” draws on one week of production traces from June 2026. During that window, Copilot generated 761 million LLM calls, 775 million tool invocations, and processed 95 trillion tokens. Those numbers make this the first large-scale empirical look at how AI coding agents actually behave in the wild, not in controlled benchmarks or synthetic tests. The cache problem nobody planned for In a typical Copilot session, users issue sparse “turns,” which are essentially prompts or instructions. Each turn kicks off an autonomous loop where the LLM makes calls and executes tools at roughly a 1:1 ratio. The median session involves about 15 LLM calls per turn, with the mean exceeding 40 in some scenarios. About 87% of all LLM calls are agent-initiated, not user-init...