GitHub extended its Copilot usage metrics API to break out activity from agent apps specifically, separate from the more general chat and completion metrics it already tracked, giving engineering organizations a way to measure how much work is actually flowing through autonomous or semi-autonomous coding agents versus traditional inline-completion and chat-based assistance. That distinction matters a lot more than it might initially sound, because agent-driven work, an AI agent picking up a ticket, making a multi-file change, and opening a pull request largely unsupervised, has a fundamentally different risk and value profile than a developer accepting an inline code completion suggestion, and organizations that lump both into one undifferentiated "AI usage" number lose the ability to reason about either one clearly. Being able to see agent app activity as its own metrics stream lets engineering leaders answer more specific, more useful questions: which teams are actually delegating meaningful chunks of work to autonomous agents versus just using AI as a faster autocomplete, whether agent-driven pull requests have different review times or revert rates than human-authored or completion-assisted ones, and where agent adoption is growing fastest so that governance and review processes can be adjusted accordingly before problems accumulate. For platform teams building internal reporting on top of the Copilot API, this is worth incorporating promptly, since as agentic coding tools take on a larger share of day-to-day engineering work, the ability to distinguish agent-originated changes from human- or completion-assisted ones in metrics and dashboards is likely to become a baseline expectation for any credible AI-adoption reporting, not a nice-to-have.