The prototype works. Your team still has to live with it

Explyt is a code cleanup harness for turning AI-generated, legacy, and inherited code into software a team can safely maintain. That job becomes urgent when an outsourcing team takes over an AI-built product with real users: a rewrite is usually off the table, while blind refactoring can break behavior the client already depends on. A general coding agent can move fast through the repository, but source text and a passing command do not establish which code is load-bearing or how far a cleanup patch reaches. Explyt grounds each bounded improvement in resolved usages, the dependency versions in use, observed runtime behavior, IDE refactorings, configured checks, and inspections, so feature work can continue while the team repairs one risky area at a time.

The useful unit is the whole cleanup loop:

map the risky area → preserve current behavior → make a bounded change → run it → inspect the final patch → repeat

Start with one change the client actually needs

A repository-wide request such as "clean up this AI-generated project" is too vague to verify. It invites another agent to rearrange large amounts of code while the team is still learning which oddities are load-bearing.

Start with a real maintenance task. Perhaps the client wants to add a payment provider, but three services calculate fees differently. Or a production incident points to retry logic copied across several modules. The task gives the cleanup a boundary and a way to decide whether the code is better afterward.

Before editing, the agent needs to find the real implementation and its blast radius. This starts with building a working model of the unfamiliar repository. Text search is useful for names and literals, but generated code is full of repeated names and near-duplicates. Explyt can use JetBrains symbol navigation and Find Usages to follow resolved declarations and callers. It can inspect the structure of large files without loading every line into the model, and read attached source or decompiled code for the dependency version connected to the project.

That does not produce an architecture document by itself. A senior engineer still has to decide which behavior belongs to the product and which duplication should disappear. The IDE facts make that decision less dependent on plausible-looking text matches.

Preserve behavior before improving the shape

The most dangerous cleanup is a beautiful refactor around misunderstood behavior.

If the relevant behavior has tests, run them first through the project's existing setup. Explyt can use supported JetBrains Run Configurations, including their selected SDK, module, arguments, environment variables, profiles, and VM options. This matters when a generic shell command does not reproduce the application the same way the client's developers run it.

When tests are missing, the team may need characterization tests before changing the implementation. Explyt can generate ordinary tests from code context. For difficult Java or Kotlin behavior, Test Generation from Execution can capture a selected call from a real run and use its arguments, result or exception, call hierarchy, and object state as the starting point for a test. The fuller test-generation workflow explains how that observed call continues through configured execution and coverage feedback.

A captured execution is evidence about one run. It may preserve a bug, include an irrelevant implementation detail, or contain sensitive data. Review the fixture, choose assertions that express behavior worth keeping, and replace secrets or personal data before committing anything.

Coverage then shows where the safety net is thin. Explyt's Increase Test Coverage workflow can run relevant tests with instrumentation, expose line and branch gaps, and let the agent target a missing path. A coverage percentage still says nothing about assertion quality. It tells the team where code executed, not whether the right contract was checked.

Keep each cleanup patch inside a hard boundary

Once the team understands a slice of behavior, it can let the agent edit that slice without handing over the whole repository.

Explyt's Edit Scope restricts writes to selected files and directories. The agent may read the wider project to understand callers and conventions, but an attempted change outside the allowed scope is rejected by the plugin.

This is useful during takeover work. A request to consolidate fee calculation should not quietly rewrite CI configuration, shared authentication helpers, and another team's module. The same preventive controls used for new AI-generated changes make the resulting diff easier to review because the developer chose its maximum footprint before generation began.

This is not a theoretical cleanup risk. In AgentLens, six trajectory reviews found real behavior or API drift: route changes broke unrelated endpoints, validation contracts widened, and apparently tidy refactors changed null handling or pagination semantics. Small cleanup steps make each of those changes easier to isolate, challenge, and rerun against the behavior the team meant to preserve.

Edit Scope cannot make a bad boundary safe. If the change genuinely requires a caller outside the selected files, the agent must stop and the developer must expand the scope deliberately. It also does not detect a logically wrong edit inside an allowed file.

For structural changes, Explyt can call JetBrains refactorings rather than imitate them with search and replace. Rename and safe deletion use the IDE's resolved project model and can update supported references across the project. This helps when repeated generated names make text replacement risky, or when removing one apparent duplicate requires proving that no code still calls it.

Framework metadata, reflection, generated sources, dynamic configuration, and unsupported languages can still hide references from static analysis. Review refactoring previews and run the affected application paths.

Investigate failures with the running program

Cleanup often breaks something the original code never documented. A test now chooses a different implementation. A proxy has a runtime type the source-level theory missed. State becomes wrong several frames before the failing assertion.

At that point, adding logs and asking the model to try another explanation can create more cleanup work. Explyt can launch a supported Run Configuration under the JetBrains debugger, stop at a breakpoint, inspect values and the call stack, and follow the runtime implementation behind an interface. The debugging article walks through this loop in detail.

Debugger evidence is scoped to the execution you reproduced. It does not prove every deployment path, and optimized or remote code may expose less state. It does help the team reject a wrong hypothesis before building another patch on top of it.

Review the repository you are about to hand back

Passing tests are necessary, but a cleanup patch can pass its selected tests and still leave suspicious null handling, framework mistakes, accidental API changes, or dead code elsewhere in the diff.

Explyt's review workflow gives the original task and resulting changes to a separate Review agent, combines model reasoning with JetBrains inspections, and records findings against files and lines in the Review Results Window. Accepted findings can go back through Fix with agent, followed by another run of the affected checks. The code review article covers that handoff and its limits.

For supported Java and Kotlin projects, Vulnerability Search can also trace supported source-to-sink patterns before and after remediation. The vulnerability-analysis workflow complements the client's established SAST and CI controls; it does not replace custom corporate rules, threat modeling, dependency scanning, or a security review.

The final diff still belongs to the engineer accepting the handover. IDE inspections can find formal problems. A model can compare the patch with the task. Neither knows every undocumented business rule or operational constraint.

Cleanup is a sequence of small proofs

An outsourcing team rarely receives enough time or product knowledge to certify an inherited repository all at once. It can make a narrower claim after each slice:

• these are the resolved callers we inspected;
• these tests preserve the behavior we chose to keep;
• this patch stayed inside its agreed boundary;
• the configured run still works;
• coverage, inspections, review, and any required security checks were rerun;
• a developer reviewed the final diff.

That sequence lets the team improve an AI-generated repository gradually while feature work continues. It also leaves artifacts the next engineer can use: focused tests, smaller symbols, explicit boundaries, review findings, and patches tied to concrete maintenance tasks.

As a code cleanup harness, Explyt lets the takeover team hand back more than a cleaner-looking diff. For every repaired area, the team can point to the resolved callers it inspected, the behavior it preserved, the enforced change boundary, the configured checks it reran, and the final reviewed patch. Those artifacts make fragile AI-generated or inherited code easier to understand and safer to change while feature work continues; a confident agent summary alone cannot provide the same maintenance trail.

Install Explyt from JetBrains Marketplace or check the current feature matrix before choosing the IDE, language, and workflow for a pilot.