Junie: how to review code when JetBrains’ coding agent plans, writes, and tests inside your IDE

2026-04-30 · 5 min read · ZenCode

Junie is JetBrains’ autonomous coding agent, built directly into IntelliJ IDEA, PyCharm, GoLand, and the rest of the JetBrains IDE family. Unlike JetBrains AI Assistant, which provides inline completions and chat, Junie is a full agentic loop: you give it a task in natural language, it reads your codebase, proposes a plan, waits for your approval, then executes — writing files, running tests, and iterating until the task is done or it gets stuck. For the tens of millions of developers already working inside IntelliJ or PyCharm, Junie does not require a context switch to a new tool. It arrives where you already are.

That seamless arrival is precisely what makes Junie interesting to analyze from a code-review perspective. Devin, Google Jules, and Sweep all deliver their output through a PR interface — a foreign surface that, by its very foreignness, reminds you to evaluate rather than accept. Junie’s output appears inside the same editor, in the same diff view, in the same environment where every deliberate change you have ever made to this project also appeared. The review traps that follow are a direct consequence of that integration.

The three Junie review traps

1. Plan-approval confidence transfer

Junie’s workflow has an explicit checkpoint that most agentic tools omit: before writing a line of code, it presents a natural-language plan and waits for your approval. The plan describes what it intends to do — which files it will modify, what logic it will introduce, how it will structure the solution. You read the plan, decide it looks reasonable, and click to proceed.

The trap is that approving a plan is not the same as reviewing code. The plan is a description of intent. The code is the implementation. These are two different objects, and correctness belongs to the second, not the first. A plan can be entirely reasonable — “add a cache layer to the user lookup function” — and the implementation can still contain a race condition, a missing invalidation path, or an assumption about serialization that does not hold. But because you have already said yes once, the diff that arrives afterward carries a residual sense of pre-approval. You are reviewing something you already agreed to, and that prior agreement compresses the review.

The fix is to treat plan approval and code review as two separate events with two separate questions. At approval time, the only question is: does this plan describe the right approach at the task level? When the code arrives, start the review from zero. The code is not pre-approved by the plan. It is a first-time submission from an external contributor who happened to describe their approach beforehand. The plan gives you useful context for understanding what the code is trying to do; it does not give you grounds to shorten the time you spend verifying that it does it correctly.

2. IDE-native trust amplification

JetBrains users tend to have deep IDE investment. Years of refactoring with Shift+F6, extracting methods with intention actions, navigating with Ctrl+B, running inspections from the editor — these operations have accumulated into a strong association: actions that happen inside the IDE are deliberate, reviewed, and understood. When you apply a refactoring in IntelliJ, the IDE performs it mechanically and correctly; the only question was whether to trigger it. That history of reliable, intention-driven IDE actions creates a context in which anything appearing in an IntelliJ diff view inherits a degree of trust by association.

Junie’s output appears in exactly that diff view. The files Junie modified show up in the VCS changes pane. The diffs render in the same comparison editor you use for every other change in the project. There is no visual signal that distinguishes “a change I made deliberately” from “a change an autonomous agent made based on a statistical model.” Compare this to GitHub Copilot agent mode, which at least operates in VS Code where some proportion of users treat the environment with less accumulated trust. For a developer who has been inside IntelliJ for five years, the cognitive association between “diff in this editor” and “intentional, understood change” is very strong — stronger than any PR interface from an external async agent could produce.

The mitigation is a deliberate continuity break before reviewing Junie’s diff. Close the diff view. Open a terminal or a separate note. Write in one sentence what you expect the change to look like — what new function should exist, what the key data flow should be, what the main condition should test for. Then open the diff and compare what you see against what you wrote. This forces an independent evaluation before the IDE context can activate the association between “this diff view” and “trusted, deliberate change.” It costs thirty seconds and interrupts the most reliable trust-injection mechanism Junie has access to.

3. Integrated test runner authority

JetBrains IDEs have some of the most deeply integrated test runner tooling in the industry. JUnit results appear in a structured panel with green and red indicators, stack traces linked directly to source lines, and a visual coverage overlay if the run was instrumented. When tests pass, the IDE communicates that pass through a familiar, trusted interface that you have watched confirm good code hundreds of times before.

When Junie completes a task, it typically runs the relevant test suite and reports the result. If tests pass, the Run tool window shows green. That green bar, rendered by the JetBrains test runner you have used throughout your career, carries substantial authority. It feels like validation by a trusted third party rather than what it actually is: a self-selected subset of tests run by the agent that wrote the code, reported through a familiar visual that borrows its authority from every legitimate test run that ever appeared in that panel.

The specific risk is test selection. Junie runs tests that existed before it began or that it wrote as part of the task. It does not guarantee coverage over every new code path. A function that handles a new error condition may have no test at all; the test suite passes because no existing test exercises the new branch. The green bar reflects the state of the tests that ran, not the state of the code that was written. GitHub Copilot agent mode has a structurally identical problem, but the JetBrains test runner’s visual familiarity amplifies the false confidence beyond what a CI log output would produce.

After Junie reports completion, run the full test suite yourself from the IDE — not the subset Junie ran, the full suite. Then open the coverage report and look explicitly at which lines in Junie’s new code are not covered. Any uncovered branch in agent-written code is a review obligation, not an acceptable gap. The coverage view is available in the same IDE; using it after every Junie task takes two minutes and makes the green-bar problem visible before it becomes a shipped defect.

How to use Junie without outsourcing your judgment

None of these traps are arguments against using Junie. For developers already living inside JetBrains IDEs, an agent that understands the project structure, proposes plans before acting, and runs tests on completion is a meaningful productivity tool. The traps are properties of the integration model — the plan-then-execute loop, the native diff view, the familiar test runner — that create specific review failure modes worth anticipating.

The practical posture is: separate plan approval from code review with an explicit mental reset, evaluate Junie’s diff as if it arrived from an unknown contributor rather than from your own IDE environment, and treat the test runner result as a starting point for your own coverage check rather than a completion signal. Each of these adjustments costs under a minute. Together they prevent the three ways that Junie’s seamless IDE integration can compress the review work that the generated code still requires.

Junie is JetBrains betting that the best place for an autonomous agent is inside the tool developers already trust completely. That bet is correct for productivity. It creates a review challenge that requires deliberate counter-habits to manage.

Related reading: JetBrains AI Assistant covers the inline completion traps in the same IDE environment. GitHub Copilot agent mode is the closest structural analog — an in-IDE agent with terminal access and a similar test-validation trap. Cursor Background Agents explores the temporal-gap trust problem that applies when agents work asynchronously. Kiro examines spec-driven IDE agents, another plan-before-code workflow. For a full comparison of AI code tools, see the best AI coding tools 2026 roundup.