Faster Developers Don’t Make a Faster Team - The JetBrains Blog

Engineering organizations are rapidly adopting AI agents, with many developers using them daily, but faster individual coding has not consistently translated into faster team delivery. Interviews across industries identify three recurring obstacles: agent practices and configurations are often isolated, inconsistently shared, and unevenly understood; increased implementation speed shifts bottlenecks to planning, code review, and verification; and automating routine tasks introduces infrastructure, maintenance, ownership, and governance challenges. Teams may produce more code and pull requests, yet reviewers and product processes can struggle to evaluate and prepare work at the same pace, potentially reducing rather than improving overall efficiency. The author argues that effective team-wide AI use requires shared context, reusable skills, review processes suited to agent-generated output, and collectively managed automation infrastructure, themes that will be explored in future posts promoting JetBrains Air Teams.

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