Hands-On Air with Dmitry Jemerov and Grigorii Kargin
In a Nutshell
Dmitry Jemerov and Grigorii Kargin demonstrate JetBrains Air, a new agentic development environment that runs AI agents (Anthropic, OpenAI, Google, Juni) in isolated workspaces or Docker containers while retaining IDE navigation and search features for code review. The core workflow centers on plan mode for complex tasks, agent-generated code review by a second agent, and worktrees for parallel development, with the team stressing that reviewing AI-generated code remains essential for maintainable projects. Air is currently Mac-only with Windows/Linux and ACP support planned for May, positioning it as the preferred tool when agents handle 80% of coding versus traditional IDEs for manual editing.
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Dimitri hosts the live stream with Grigori Kargin, team lead of Air at JetBrains. Grigori has worked at JetBrains on multiple IDEs and previously on Kotlin. He is currently studying linguistics at the University of Leiden and has been working on a linguistic database project. He is exploring JetBrains' AI development tools.
Grigori explains that Air is an agentic development environment centered around agent experiences. It is agent agnostic and supports agents from Anthropic, OpenAI, Google, and JetBrains' own agent Juni. ACP support is planned to allow any ACP-compatible agent.
The installation is performed through Toolbox. Air is currently available on Mac, with Windows and Linux internal builds ready and planned for release in May. ACP support will appear in the nearest build under a feature flag, with full marketplace support coming.
Users can use multiple subscriptions simultaneously, including JetBrains AI, OpenAI, and Anthropic keys. This allows using both cloud agents and local agents together.
Air presents an onboarding walkthrough. Environment options include local workspace, Git worktree, and Docker. Local workspace uses the main copy of the project, while Docker provides isolation for running multiple application instances without conflicts.
Reviewing AI-generated code is emphasized as essential. For code that will persist in a project, understanding the structure prevents confusion when issues arise. This differs from one-off scripts where review may be optional.
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