Most engineering organizations can say what they spend on coding agents. Fewer can say what that spend buys. Do AI-assisted pull requests merge faster than the rest, or do they pile up waiting for review? Which teams and repositories have picked the agents up, and which tools, MCP servers, and skills do those agents lean on? The answers sit between agent telemetry and your pull requests, and they are hard to read together.
AI SDLC Insights is the reworked successor to AI Coding Insights, and it reads them together. The Overview leads with total spend, active users, cycle time, average cost per user, and the share of assisted pull requests. Flow through the pipeline shows where merged pull requests slow down between coding, review, and merge, and Assisted vs non-assisted cycle time compares the two at the percentile you choose. Cost, Adoption, Productivity, and Tools and skills each go deeper, with pull request metrics such as time to first review, pull requests awaiting first review, and review depth per merge. Every entry in Sessions opens a details page with its prompts, tools, and conversation. Links into AI Coding Insights redirect to the new page.