Clean Up Agent-Written Code

Agent-written code often lands in a usable state, not a finished one. It may pass a first test run and still have awkward structure, duplicated logic, weak names, and hidden assumptions. The skill is not making the agent smarter. It is turning rough output into code a team can maintain.

That changes where the cleanup happens. A human no longer writes every line, so review shifts toward structure, boundaries, and invariants. Treat agent output like a draft and refactor it the way you would a junior engineer’s first pass: tighten scope, remove incidental complexity, and check behavior at the edges.

What usually needs cleanup

The common failure modes are predictable. Agent output often:

• repeats logic across files instead of extracting a shared path
• uses broad functions that mix parsing, business rules, and I/O
• names things by shape rather than intent
• adds extra abstraction too early
• handles the happy path well but leaves edge cases implicit
• makes changes that work locally but are hard to review later

None of that is unique to agents. The difference is volume and speed. Agents can produce a lot of code quickly, which makes structural debt show up sooner.

Start with the smallest stable slice

The best cleanup pass usually starts by finding the smallest part of the change that must remain true. Before refactoring, ask what behavior is essential and what is just implementation noise.

A practical sequence is:

1. Identify the user-visible behavior or invariant.
2. Separate that from helper code, formatting, and incidental branching.
3. Keep one path working while you simplify the rest.
4. Re-run tests after each structural change.

This is slower than rewriting everything at once, but it reduces the chance that cleanup breaks the one thing the agent got right.

Refactor around seams, not style

A lot of cleanup is really style correction. That is not enough. The better target is the seam where responsibilities meet.

Look for places where the code crosses between:

• input parsing and domain logic
• domain logic and persistence
• orchestration and side effects
• validation and transformation

If an agent mixed those layers together, split them apart. That makes later changes cheaper and review easier. It also gives the next agent a clearer boundary if you hand the task back to automation.

One practical pattern is to keep the first pass in a single file or module until the behavior is stable, then extract only the parts that have a clear reason to exist. Premature extraction can make the code harder to follow than the original draft.

Use tests as a refactoring guardrail

Refactoring agent-written code without tests is mostly guesswork. You do not need exhaustive coverage, but you do need a few checks that protect the behavior you care about.

Useful test types include:

• one happy-path test for the main flow
• one edge-case test for invalid or missing input
• one regression test for the bug or ambiguity that triggered the change

If the code is already messy, add the tests before the cleanup. That gives you a stable reference point. If the code is simple and the change is small, you can sometimes refactor first and test immediately after. The key is not the order in the abstract. It is whether you can tell when you broke something.

Keep the agent on a short leash

When you ask an agent to refactor its own output, be specific. Vague prompts tend to produce more abstraction, not better structure.

Useful instructions are concrete:

• remove duplicated parsing logic
• split I/O from pure transformation
• preserve behavior exactly
• keep public interfaces unchanged
• explain any new helper in one sentence

If you want a tool to help with cleanup, use it for one narrow pass at a time. For example, ask it to isolate validation, then review that change before asking for naming cleanup. This reduces compounding errors.

Tradeoffs and limits

There is a real tradeoff between cleanup and momentum. Over-refactoring can erase the speed advantage of agentic coding. If the code is short-lived, internal, or clearly experimental, a lighter cleanup may be enough.

There is also a limit to how much structure an agent can infer from context alone. If the domain rules are unclear, the refactor may simply rearrange ambiguity. In that case, the right move is not more cleanup. It is to write down the rule first.

Another limit: some code is messy because the problem itself is messy. A refactor can improve readability without making the system simpler. That is still useful, but it should not be mistaken for design progress.

A practical review habit

A good review question is: if this change failed in production, where would I look first? If the answer is “everywhere,” the code is still too entangled.

That is why cleanup should aim for obvious fault lines. Clear seams make rollback, debugging, and future agent edits easier. This is also where a short design pass helps. In our methodology, that maps to the Review step: check the structure against the behavior you actually need, not the shape the agent happened to produce.

A simple workflow that holds up

For most teams, a workable loop looks like this:

• let the agent draft the change
• add or confirm the smallest useful tests
• refactor for seams and names
• re-run the tests after each structural edit
• stop when the code is easy to explain in one pass

That last line matters. If you cannot explain the code without narrating every branch, it is probably still carrying too much incidental complexity.

Agent-written code does not need to be perfect on arrival. It needs to be easy to clean up without losing the behavior that matters.