Track AI assistant activity patterns with detailed GitHub Copilot and Cursor insights

Rolling out tools like GitHub Copilot and Cursor is just the first step in incorporating AI into your engineering work. If you want to see real productivity gains, people need to build new habits and learn to use these tools effectively. That requires visibility into how your teams are actually using them.

Now you can see detailed activity patterns for GitHub Copilot and Cursor across your organization. Track engagement levels, understand where AI suggestions are gaining traction, and identify teams that might need extra support. You can also evaluate different AI tools to make informed decisions about which ones deliver the most value.

What you can see

Daily engagement charts show how many of your active users are accepting code suggestions. This helps you understand whether people are just trying out the tools or actively relying on them in their work.

Activity volume charts display the number of suggestions and acceptance rates over time, giving you insight into how much your teams are using AI assistance.

GitHub Copilot insights include breakdowns by editor and programming language, so you can spot patterns in how different tools and codebases benefit from AI suggestions.

Cursor insights show activity across different modes (agent, chat, and ⌘K/Ctrl+K), helping you understand which features resonate most with your teams.

Getting started

Check that GitHub Copilot and Cursor are connected in settings → integrations → AI assistants, then navigate to insights → AI assistants to explore activity patterns for each tool.