JetBrains Air vs Claude Code vs Cursor: Which Agentic Development Environment Should Your Team Use?

Three CTOs asked me the same question last week: should we switch to JetBrains Air? It launched on March 9th, JetBrains is calling it an "agentic development environment," and it lets you run Claude, Gemini, Codex, and Junie side by side in one workspace. That's a genuinely new idea. But whether it's the right idea for your team depends on how you think about agent coordination, context, and where things break.

I've spent the past week running all three, Air, Claude Code, and Cursor, on the same set of tasks: a medium-sized Next.js refactor, a Python data pipeline, and a greenfield API service. Here's what I found.

What JetBrains Air Actually Is

Air isn't an IDE plugin. It's a standalone environment (currently free and macOS-only in public preview) where multiple AI agents work in parallel on your codebase. You can have Claude Agent refactoring a module in one pane while Gemini CLI writes tests in another, and Junie handles a third task simultaneously.

The context model is interesting: instead of pasting code blocks, you reference specific symbols, methods, lines, or commits, and Air resolves them against your codebase. It also includes an integrated terminal, Git, and a built-in preview server, so you don't need to alt-tab to verify changes.

The multi-agent angle is what makes it genuinely different from everything else on the market. Claude Code and Cursor both run a single primary agent (with subagents in Claude Code's case). Air treats the agent as interchangeable and lets you run several concurrently.

Multi-Agent Coordination vs Single-Agent Depth

This is the core architectural split, and it drives most of the practical differences between these three tools.

Air gives you breadth. You can assign different agents to different tasks and they'll work in parallel. During my testing, I had Junie generating database migrations while Claude Agent wrote the API handlers that would use them. The coordination is manual (you're the orchestrator), but the parallelism is real. Where it gets tricky is when agents step on each other's changes, since Air doesn't currently have a conflict resolution mechanism beyond Git.

Claude Code goes deep instead of wide. One agent, one context window, accumulating understanding turn by turn. When I pointed it at the Next.js refactor, it spent several turns reading the codebase before touching anything, then made changes that accounted for import chains three files deep. The Pragmatic Engineer's 2026 survey found that Claude Code captured 46% of developers who "love" their AI coding tool, and director-level engineers prefer it at roughly twice the rate of junior engineers. That's not an accident; the single-agent depth rewards people who know how to scope tasks well.

Cursor sits in the middle. It's an editor-first experience with strong inline completions and a chat panel that can edit files. The agent mode has improved significantly, but it's still primarily optimised for shorter, focused interactions rather than long autonomous runs. Cursor hit $2B in annual revenue by March 2026, doubling in three months, so the approach clearly resonates.

// Claude Code's approach: single agent reads broadly, then acts precisely
// This is what the agent loop looks like when you watch the transcript:
 
// Turn 1: Reads package.json, tsconfig, app/layout.tsx
// Turn 2: Greps for all usages of the deprecated component
// Turn 3: Reads each file that imports it
// Turn 4: Edits all 7 files in sequence, updating imports and props
// Turn 5: Runs `tsc --noEmit` to verify no type errors
 
// The key: by turn 4, Claude has full context on every usage.
// Air's multi-agent approach would split this across agents,
// but each agent would need to independently discover the import chain.

Context Management

How each tool grounds the agent in your codebase matters more than which LLM it calls.

Air uses symbol-level references. You mention @PaymentService.processRefund and the agent gets that method's source, its callers, and its type signature. This is precise and token-efficient, but it means the agent only knows what you point it at. If a bug lives in the interaction between two modules you didn't reference, the agent won't find it on its own.

Claude Code treats your filesystem as its context store. It uses Glob, Grep, and Read tools to explore the codebase reactively, loading what it needs based on what it discovers. A CLAUDE.md file at the project root provides persistent conventions. The downside is token cost: a 30-turn session where the agent reads extensively can burn through context, and the window eventually compresses older turns.

Cursor indexes your codebase locally and uses embeddings for retrieval. The @codebase reference pulls in relevant files automatically. It's the most seamless for quick questions ("where is this function defined?"), but for complex multi-file changes the retrieval can miss files that are semantically distant but structurally important.

# How you'd set up the same task in each tool:
 
# Air: explicit symbol references
# @DatabasePool.getConnection @RetryPolicy.execute
# "Refactor getConnection to use the retry policy"
 
# Claude Code: CLAUDE.md + natural language
# "Refactor the database connection logic to use our retry policy.
#  The retry policy is in src/infra/retry.py"
 
# Cursor: @codebase + chat
# @codebase "Refactor database connections to use retry policy"
# Cursor's embeddings find both files automatically (usually)
 
# The tradeoff: Air is most precise, Claude Code is most thorough,
# Cursor is fastest for the common case.

LLM Flexibility

Air is model-agnostic by design. Claude Agent, Gemini CLI, Codex, and Junie all run natively, with more agents coming via the Agent Client Protocol. You can literally try the same prompt against three models in parallel and compare results. For teams evaluating models or working with clients who mandate specific providers, this is a genuine advantage.

Claude Code runs Claude exclusively. You get Opus, Sonnet, or Haiku depending on your configuration, but you're locked into Anthropic's model family. The upside is deep optimisation: Claude Code's tool definitions, system prompts, and context management are all tuned specifically for Claude's behaviour. The single-model focus is why it handles long agentic sessions more reliably than tools that bolt agent capabilities onto multiple backends.

Cursor supports multiple models (Claude, GPT, Gemini, and others) and lets you switch per-request. In practice, most teams settle on one model for consistency, but the option to fall back is useful. Addy Osmani calls this "model musical chairs": when one model gets stuck, you switch to another.

Production Reliability

This is where the differences get sharp.

Air is in public preview, and it feels like it. I hit several rough edges during testing: agent sessions that lost context after a long idle period, occasional UI freezes when multiple agents were writing simultaneously, and no clear way to recover a failed agent run mid-task. JetBrains will fix these; they're an engineering-tools company with decades of polish behind IntelliJ. But right now, I wouldn't put Air in front of a team that needs to ship daily.

Claude Code is the most reliable for long autonomous runs. The agent loop is deterministic in structure (prompt, evaluate, act, verify, repeat), and because everything accumulates in one context window, you can trace exactly what happened when something goes wrong. Session resumption works, budget caps prevent runaway costs, and the permission model gives you fine-grained control over what the agent can touch. The failure mode is usually "the agent got confused and needs a clearer prompt," not "the tool crashed."

Cursor is the most polished editor experience. It rarely crashes, inline completions are fast, and the UX for accepting or rejecting changes is excellent. The agent mode is less reliable for long multi-file tasks than Claude Code, but for the "edit this function, write a test, move on" workflow that most developers actually do most of the time, it's arguably the best tool available.

When to Pick Each Tool

Pick Air if you're an engineering lead who wants to evaluate multiple AI models side by side, your team already uses JetBrains IDEs, or your tasks decompose cleanly into independent parallel workstreams. Give it six months for the preview rough edges to smooth out before committing a whole team.

Pick Claude Code if your work involves complex, multi-file changes that require deep codebase understanding, you want the agent to run autonomously for 20+ turns, or you're building CI/CD pipelines that include agentic steps. The single-agent depth model rewards clear task scoping, and the survey data suggests experienced engineers get the most out of it.

Pick Cursor if your team values editor integration and fast iteration over long autonomous runs, you want AI-assisted development without changing your existing workflow, or you need model flexibility without managing multiple tools. It's the lowest-friction option for teams adopting AI coding tools for the first time.

What I Actually Use

Claude Code for anything that requires understanding a codebase across more than two or three files. The depth of context it builds over a multi-turn session is still unmatched, and the ability to run it in CI pipelines (code review agents, automated refactoring) makes it useful beyond interactive development.

Cursor for quick edits, one-off scripts, and anything where I want to stay in an editor. The inline completions are genuinely good, and for focused tasks it's faster than starting a Claude Code session.

I'll be watching Air closely. The multi-agent coordination model is the right long-term direction for complex projects where different parts of the codebase can be worked on independently. But it needs to mature before I'd recommend it for a team that's shipping production code on a deadline. When it gets Linux support and a stable release, I'll revisit this comparison.

If you're evaluating agentic development environments for your team and want help running a structured comparison on your actual codebase, that's something we do.

Sources

• JetBrains Launches Air and Junie CLI for AI-Assisted Development — InfoWorld's coverage of Air's public preview launch and multi-agent architecture.
• AI Tooling 2026 — Pragmatic Engineer survey of 906 developers on AI coding tool adoption, satisfaction, and usage patterns.
• The AI Coding Workflow — Addy Osmani's practical patterns for AI-assisted development, including multi-model approaches and verification strategies.

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