Stop AI-generated cleanup work before it enters the codebase

Explyt is a code cleanup harness for bringing AI-generated, legacy, and inherited code under engineering control. The cheapest cleanup starts before new agent-written debt enters that codebase: an agent can edit beyond the task, validate against the wrong setup, or leave a final patch nobody checked independently. A prompt may ask it to stay within two files and run the right tests, but the model still decides whether those instructions become actions. Explyt replaces that trust with enforced write boundaries, checks run in the configured JetBrains environment, and a separate review of the resulting diff, so the team sees out-of-scope attempts and verification evidence before accepting the patch.

defined write scope → implementation → configured tests and measurable checks → final review → human-approved diff

A prompt is guidance, not a write boundary

Developers already tell agents to "change only these files" or "do not touch production code." Strong models usually follow such instructions. Usually is not a control, though. A model may decide that the cleanest implementation requires one more helper, a shared interface, or a build file.

That initiative can be useful when the task genuinely needs a wider change. It becomes expensive when the developer intended a narrow patch and notices the expansion only after several files have been rewritten.

AgentLens found unstable process or scope control in 15 of 32 trajectory reviews. Agents skipped confirmation gates, widened focused tasks, created throwaway test files, and sometimes edited files outside the allowed area. A hard boundary turns those failures from something the developer discovers in the final diff into a blocked action that must be resolved deliberately.

Explyt's Edit Scope turns the intended boundary into an enforced one. You attach the files or directories the agent may edit and enable Edit Scope. The agent can still read the rest of the project to understand the task, but an attempt to write outside the allowed scope returns an error from the plugin.

For the billing change, the developer might allow:

billing/src/main/java/com/acme/billing/validation/
billing/src/test/java/com/acme/billing/validation/

The agent can inspect callers, interfaces, and neighboring modules, using the same indexed navigation that supports understanding an unfamiliar repository. It cannot quietly change them. If the implementation really requires a shared contract update, the blocked write becomes a decision point: expand the scope deliberately, revise the plan, or split the work into another task.

Teams can also use .writeignore to protect paths that should remain unavailable for edits. Explyt shows proposed changes in the JetBrains diff viewer, where the developer can accept, reject, or edit them before applying.

A boundary cannot make the allowed edits correct. It prevents one class of damage: unapproved changes outside the area the developer opened for the task. A scope that is too broad provides little protection; a scope that is too narrow may block a legitimate solution. The developer still chooses the boundary.

Make the agent prove progress while it is still working

The next source of cleanup debt is premature success. The agent writes plausible code, runs a convenient check, sees green output, and stops. Later, the team discovers that it used the wrong project environment, skipped the relevant suite, or added tests that execute little of the changed behavior.

Explyt can run supported builds and tests through existing JetBrains Run Configurations. That keeps the selected SDK, module, working directory, VM options, environment variables, and framework profile connected to the same project in which the agent edited the code.

This matters most in enterprise repositories where the working local setup already lives in the IDE. A shell command can still be the project's authoritative workflow, and Explyt should not replace it merely to use an IDE tool. But when the reproducing setup is a Run Configuration, reconstructing it manually creates another chance to test a different environment from the one developers use.

Tests also need a measurable target. Explyt's Increase Test Coverage workflow can run relevant tests with IDE coverage instrumentation, show uncovered lines and branches, and let the agent add targeted tests before rerunning the measurement. For state that is difficult to reconstruct, test generation from a real execution can provide the starting fixture.

The loop is concrete:

1. Implement the scoped change.
2. Run the relevant configured tests.
3. Inspect compilation errors, failures, and coverage gaps.
4. Add or correct a focused test.
5. Rerun until the agreed check passes or the agent reports why it cannot.

A team can ask for a formal threshold such as 90% coverage where that metric is appropriate. The useful part is the feedback loop, not the number itself. Coverage proves execution, not correctness. A test can cover a branch with a weak assertion, preserve the wrong behavior, or mock away the code that matters.

For that reason, the gate should match the risk. A validation change may need focused unit tests and branch coverage. A database migration may need an integration environment that IDE coverage cannot provide. A bug whose cause depends on live state may require debugger evidence. Security-sensitive code may require vulnerability analysis, dependency policy, and human security review. Explyt supplies evidence from supported tools; it does not invent a universal definition of done.

Check the final patch, not the agent's memory of its work

An agent may run useful checks during implementation and still leave a flawed final diff. The last fix can invalidate an earlier test result. A test added late can contain a weak assertion. The agent's summary describes what it believes it did, while the repository contains what it actually did.

Use Auto Review as a short final barrier after the coding loop. Give the Review agent the original request and resulting changes, then inspect its findings together with JetBrains inspection results in the Review Results Window. Accepted findings can go back through Fix with agent, followed by the affected checks again.

The separate article Your coding agent finished. Now review what it actually changed covers that workflow in detail. Here its role is deliberately smaller: catch defects in the final patch after scope and test controls have already reduced the amount of cleanup work.

Auto Review does not replace a human reviewer, and running the authoring model twice does not guarantee independent judgment. IDE inspections also have false positives and cannot validate business intent. The developer still reviews the diff and decides whether the evidence is enough to merge.

Prevention is cheaper when the controls are explicit

There is no setting that makes generated code maintenance-free. Requirements can be wrong, tests can encode the wrong contract, and a perfectly scoped patch can still be a bad design. Teams will continue cleaning up inherited AI-generated code, especially when projects were built without these controls.

The practical goal is to avoid creating predictable cleanup work during every new agent run.

As a code cleanup harness, Explyt uses these controls to keep the next AI-generated patch from adding avoidable debt to code the team is already trying to stabilize. The acceptance record is concrete: the final diff stayed inside its approved boundary, the relevant configured checks passed after the last edit, and a separate reviewer inspected the result. The developer still owns the design decision, but scope and basic process compliance no longer have to be inferred from the agent's completion message.

Install Explyt from JetBrains Marketplace, then configure Edit Scope and the test coverage workflow for your next agent-assisted change.