JetBrains Air: The Agentic IDE That Orchestrates Multiple AI Models at Once
JetBrains launched Air — a new agentic development environment that runs Codex, Claude, Gemini, and Junie concurrently. We analyze what it does differently, how it compares to Cursor and Copilot, and whether it delivers.
Introduction
JetBrains — the company behind IntelliJ, WebStorm, and PyCharm — just launched something that is not another plugin or chat sidebar. Air is a fundamentally new development environment built from scratch around AI agents. Not one agent. Multiple agents running concurrently, each with different capabilities.
The premise is bold: instead of picking between Cursor, GitHub Copilot, Claude Code, or Gemini CLI, why not orchestrate all of them? Air coordinates multiple AI providers simultaneously, routing tasks to whichever model handles them best.
At MG Software, we have been using Cursor as our primary editor since 2025. The question is: does Air's multi-agent approach actually deliver better results than a single well-configured AI editor? Here is what we found.
What Makes Air Different from Cursor and Copilot
The fundamental difference is architectural. Cursor and Copilot embed a single AI model (or family of models) into a VS Code-based editor. Air is a standalone environment that treats AI agents as first-class participants — not assistants sitting in a sidebar.
In practice, this means you can have Codex working on one module, Claude Agent refactoring another, and Junie (JetBrains' own agent) writing tests — all simultaneously. Air manages the context boundaries, prevents conflicting changes, and presents a unified view of all agent activity.
This is closer to how a senior developer delegates work to a team than how a developer uses an autocomplete tool. The difference matters when your project has multiple parallel workstreams that benefit from different AI strengths.
The Multi-Model Advantage (and Limitation)
Each model has genuine strengths. Claude excels at long-context reasoning and architectural understanding. Codex is strong at boilerplate and API integration code. Gemini handles multimodal tasks and documentation well. By routing tasks to the right model, Air can theoretically outperform any single-model approach. For a head-to-head analysis, see our Cursor vs JetBrains AI comparison.
The limitation is coordination overhead. When three agents touch related files, merge conflicts and inconsistent patterns emerge. Air handles this better than manually switching between tools, but it is not friction-free. In complex codebases — which is most production code — the orchestration layer sometimes introduces latency that offsets the parallelism gains.
We found the sweet spot is using Air for greenfield development and large refactoring tasks where parallelism genuinely helps. For deep focus work on a single module, a well-configured Cursor setup is still faster.
How Air Compares to Our Current Stack
At MG Software, we currently use Cursor Pro with Claude as our primary development environment. We compared it against Air over a two-week internal evaluation on a medium-sized Next.js project (around 80,000 lines of TypeScript).
Air was faster for cross-cutting changes: updating a shared component interface across 30+ files, generating comprehensive test suites, and scaffolding new feature modules. The ability to have multiple agents working simultaneously on independent subtasks was a genuine time saver.
Cursor remained superior for single-file deep work: debugging complex state management issues, optimizing database queries, and writing security-critical code where you want one high-quality model with full context rather than multiple models with partial context. For a broader view, check our comparison of the best IDE and code editors in 2026.
What This Means for the IDE Market
JetBrains Air represents a philosophical split in the AI IDE market. On one side: deep single-model integration (Cursor, Windsurf). On the other: multi-model orchestration (Air). The question is whether the future favors depth or breadth.
Our bet is that both approaches will coexist. Some tasks benefit from the focus of a single, well-tuned model. Others benefit from the parallel capabilities of multiple specialized models. The best teams will use both tools — Air for big-picture changes, Cursor for precision work. For a broader view, explore our best AI coding assistants roundup.
For businesses evaluating AI development tools, the key takeaway is this: the IDE is no longer a static text editor. It is becoming an orchestration platform for AI agents. Plan your tool budget accordingly — and invest in training your team on effective agent delegation, not just prompt engineering.
Should You Switch?
Air is in public preview, which means it is not production-ready yet. JetBrains has been upfront about known limitations: incomplete language support, occasional agent conflicts, and performance issues on very large repositories.
Our recommendation: try Air for exploratory projects and greenfield work. Keep your current setup for production codebases until Air reaches general availability. And watch this space — the multi-agent IDE concept is likely to be adopted by Cursor and VS Code within the year.
Building with AI tools and want guidance on setting up the right development environment for your team? Let us talk. We evaluate these tools daily and can help you navigate the rapidly evolving landscape.
Jordan Munk
Co-Founder
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