The Systems Behind Successful AI Adoption
Engineering leaders open up about the systems behind their agents: what workflows they built, how they control AI costs, and what you can apply now.
Nine original case studies
Code review queues grow. Agents repeat the same mistakes. AI bills rise without a clear picture of what the spending achieved.
We interviewed engineering leaders at Fin, Agentic AI Foundation, and other organizations about how they’re tackling these challenges. Together with our own experience at JetBrains, their stories show what worked and what’s still unresolved.
What's inside?
95% adoption. No faster delivery.
Widespread AI use hadn’t improved developer velocity. Discover how Angie Jones shifted the focus to delegating real tickets, with champions building team knowledge directly into repositories.
3× PR throughput. 54% fewer backlogged defects.
Fin put a dedicated team behind its agent platform, tested skills against real cases, and strengthened automated checks.
One targeted skill. Build costs cut to $0.30.
At JetBrains, an agent repeatedly rediscovered how to build a project, costing $1-2 per run. A focused skill brought that down to roughly $0.30. Learn why more context isn’t always better.
Agentic maturity model 🎁
Based on our engineering work and client conversations, we developed a framework to assess whether your team has the foundations to work effectively with agents at scale.
Learn from the people doing the work
Brian Scanlan
Angie Jones
Anatoly Chichikov
Hywel Carver
Ciara Byrne
Mikhail Filippov
Build software with agents using JetBrains Air
JetBrains Air is a system for building software with agents, supporting developers, teams, and organizations.
Explore tools for coding with agents, automating workflows, and governing AI usage and spending across your organization.