AI Agents for Clean Code: Refactoring Rules and Review

AI agents can refactor functions, rename variables, reduce duplication, and flag logic that is hard to follow. Whether the result is actually cleaner depends on what you ask for and how you check the output. Tools like fenbs let you store the naming conventions and review rules your team has agreed on, so each agent session starts with the same constraints instead of a blank slate.

The practical problem with agents and code quality is stylistic churn: changes that pass a linter but make the codebase harder to follow for the people who maintain it. Addressing that means scoping the work carefully, testing that behavior has not changed, and reviewing whether the revision is genuinely clearer.

What an AI agent can do for code quality

Coding agents work best on bounded, concrete tasks: extracting a repeated block into a named function, renaming a concept consistently across a module, removing dead code with confirmation, or rewriting a long conditional into smaller named checks. They are less reliable on subjective calls about whether a name communicates intent, because intent depends on the conventions the rest of the codebase already uses.

• Extract repeated logic into a named function with a consistent signature.
• Rename variables and parameters to match an agreed convention across a file.
• Remove code that is never called, after confirming no dynamic references exist.
• Rewrite a long conditional into a set of named helper checks.
• Add inline comments where the logic would not be obvious to a new reader.

Every one of these needs a human review before merging. An agent does not know which parts of the codebase other people are actively reading, which rename would break an API contract, or which abstraction the team chose to avoid last month. Tell it the scope and ask it to explain the reasoning before it applies any change.

Scope the refactor before the agent runs

A precise prompt gets better results than "clean this file." Name the specific concern, which files are in scope, and what the agent must not touch. For example: "Extract the three validation functions at lines 42 through 68 into a separate module, keep the existing signatures, and do not change anything else in this file."

Good scope also means telling the agent about rules your team has already settled. If the codebase uses a specific naming convention for hooks, avoids classes in favor of plain functions, or bans a particular pattern, state that at the start of the session. Without that context the agent will apply its own defaults, which may not match yours.

Limit each change to one concern. If you ask for a rename and a restructure together, it is harder to check what changed and why, and harder to revert if a test fails. Run the rename, verify it, commit it, then address the structure in a separate pass.

Test behavior, not just style

A clean-looking refactor can quietly change what code does. Run your test suite after each bounded change. If tests are missing for the function being refactored, write them first. An agent can help generate test cases, but review them before relying on them as the verification step.

Ask the agent to describe the semantic difference between the before and after versions. "I extracted the logic but the observable behavior is unchanged" is a useful answer. "I made it cleaner" is not. If the agent cannot state the invariant it preserved, that is a signal to inspect the diff more carefully.

Review the output for real clarity

Clarity is relative to the reader, not just the author. When reviewing a refactored function, ask: does the name describe what it does in terms a new team member would recognize? Does the structure match how similar operations are handled elsewhere in the codebase? A change that introduces a new pattern the rest of the file does not use can make code harder to read, even if it is more compact.

Reject changes that add new dependencies, new abstractions, or new patterns without a clear reason. Agents will sometimes introduce a utility class or pull in a helper where a direct approach would have worked. Reviewing for clarity includes checking whether the complexity added is serving the code or just satisfying a general preference.

Save the lessons when a suggestion is rejected

When the team rejects an agent suggestion, record the reason. Was it a rule violation? A wrong abstraction level? A naming convention the agent did not know? Saving that as a lesson means the next session starts with one more piece of context, and the same mistake is less likely to come back.

Rules that survive multiple reviews belong in a shared record. "We do not use global state in component files" is the kind of instruction a developer would mention in a code review but that an agent will not infer from reading the existing code. Write it once, put it where agents read it, and stop repeating it in every session.

How to do this with fenbs

Start with one agreed rule that a coding agent keeps getting wrong. Write it as a Decision, connect the agent to fenbs over MCP, and ask it to read the project context before the next refactor session.

1. Create a free account. A personal kanban board is enough to start; add teammates when you want shared rules and a shared task list.
2. Add your project. Write a short description of the codebase and its goals. Add the tasks currently open for refactoring work.
3. Write your first code rules in Decisions and rules. Include naming conventions, patterns the team avoids, and any scope limits for agent changes. Mark each as a standing rule.
4. Connect your coding agent at Integrations. Follow the guide for the tool you use — Claude Code, Cursor, GitHub Copilot, or another supported assistant — and approve read and write access.
5. Before each refactor session, ask your agent to read the project context. After the session, ask it to update the task with what changed and whether any new rule came up that the team should decide on.