If your development setup is built around IntelliJ IDEA, PyCharm, WebStorm, or any other JetBrains IDE, you’ve probably watched the AI coding assistant wars play out with a degree of detachment. Claude Code assumes you’re happy in the terminal. Cursor requires switching editors entirely. GitHub Copilot works in JetBrains IDEs but its agent mode is optimised for VS Code workflows.
JetBrains built Junie specifically for people who live in their IDEs. It’s not a chat panel bolted onto the side. It’s an agent that understands the JetBrains project model, works with your existing run configurations and test suites, and can take multi-step actions inside the development environment you already have.
What Junie Actually Does
The difference between JetBrains’ AI Assistant (which has been around for a while) and Junie is the difference between a very capable autocomplete-and-chat tool and an agent that can execute a plan.
AI Assistant handles inline completions, chat-based code questions, documentation generation, and short refactors. It’s good at what it does. Junie takes on the tasks where you’d describe a goal rather than a specific edit — “add pagination to this endpoint and update the tests to cover the new behaviour” — and executes the necessary steps to get there.
In practice, Junie reads the relevant parts of your codebase, plans the changes required, makes the edits across multiple files, runs your existing test configurations, reviews the results, and iterates if something fails. If you run a debugger profile or have lint checks set up, Junie can use those too. The execution happens in a browser-based agent panel within the IDE, so you can watch the plan unfold and intervene if it goes in a direction you didn’t intend.
The verification loop is what distinguishes a capable agent from a capable generator. Generating code that looks right is table stakes. Running your tests and using the results to drive further iteration is the piece that turns “AI-generated draft” into “something you’d actually commit.”
How It Compares to Cursor and Copilot Agent Mode
Fair question: if you’re willing to think about changing workflow, is Junie better than just switching to Cursor or using Copilot’s agent mode?
Cursor is probably the most capable agentic coding environment available right now, and it’s genuinely good. But it requires you to move your entire development setup — keybindings, plugins, the mental model of how your IDE works. For teams with heavy JetBrains IDE investment (complex run configurations, custom plugins, years of IntelliJ muscle memory), that’s a real switching cost that goes beyond just installing an application.
GitHub Copilot’s agent mode works in JetBrains IDEs, but the experience is clearly designed around VS Code first. The integration with IntelliJ project structure — understanding Maven and Gradle configurations, working with Spring run configurations, using the existing test runners correctly — isn’t as deep as a JetBrains-native implementation.
Junie’s advantage is that it runs inside the environment you already have, uses the project understanding that IntelliJ already has (symbol indexing, module structure, dependency graph), and integrates with your existing run configurations rather than trying to infer them.
The honest trade-off: if you’re starting fresh or don’t have deep JetBrains IDE investment, Cursor’s agent capabilities are competitive and the editor is excellent. If you’re a Java shop that runs IntelliJ Ultimate with specific run configurations, a Python team deep in PyCharm’s debugging tooling, or a JavaScript team using WebStorm with particular project setups, Junie’s IDE-native integration is worth taking seriously.
Language and Framework Support
JetBrains IDEs cover a lot of ground, and Junie works across the fleet: IntelliJ IDEA for Java and Kotlin, PyCharm for Python, WebStorm for JavaScript and TypeScript, GoLand for Go, RustRover for Rust, Rider for .NET and C#, CLion for C and C++.
The agent’s understanding of the project is drawn from the same indexing that powers IntelliJ’s refactoring tools. It understands that a method is called from three different places, that a class implements a specific interface, that a test fixture depends on a particular Spring context. This isn’t just codebase text — it’s the semantic structure that JetBrains IDEs have been building for decades.
This matters for refactoring tasks in particular. An agent that understands call graphs and interface implementations makes fewer mistakes when renaming, restructuring, or moving code than one that’s pattern-matching against file text.
Getting Started
Junie is available through JetBrains’ AI subscription, which also covers the AI Assistant features. The agent mode is accessed through a dedicated panel in the IDE rather than the inline completion or chat interface — you describe the task, review the plan Junie outlines, and approve execution.
For teams already paying for JetBrains toolbox subscriptions, adding AI — which includes both AI Assistant and Junie — is the path of least resistance compared to managing a separate Cursor or Copilot subscription alongside existing IDE licensing.
The workflow Junie is best suited for is the kind of task that previously would have taken you 45 minutes of focused work: adding a feature with tests, fixing a class of bugs across multiple files, updating an API integration after a schema change. For those tasks, having an agent that understands your project structure and can execute, test, and iterate is genuinely useful rather than just impressive in demos.
What to Watch
Agent reliability on complex, long tasks is still maturing across all tools in this space. Junie performs well on tasks with clear acceptance criteria — existing tests pass, lint is clean, the feature works as described. Tasks that are more subjective (improve the readability of this module, simplify this architecture) benefit from closer review, because the agent’s criteria for “done” may not match yours.
The integration with JetBrains’ code review tooling and Space (their project management platform) is something to watch as the product matures. A tighter loop between code review feedback and agent iteration would be a meaningful workflow improvement for teams using the full JetBrains ecosystem.
For now: if you’re a JetBrains shop that’s been waiting for an AI coding agent that doesn’t require changing your editor, Junie is the answer that’s actually been built for your setup.