Refactoring is essential to maintaining a healthy codebase, but it's often one of the most dreaded tasks in software development. When a rename or pattern change touches hundreds of files, the work becomes mechanical and error-prone. This is exactly where AI agents excel.
The Challenge of Multi-File Refactoring
Consider renaming a widely-used function across your codebase. A human developer needs to:
- Find all usages across potentially thousands of files
- Update each reference while considering context
- Handle edge cases like string references, comments, and documentation
- Run tests to verify nothing broke
- Create a reviewable PR with clear intent
This process is tedious, and the more files involved, the higher the chance of mistakes. A missed reference breaks the build. A hasty find-and-replace changes things it shouldn't. The cognitive load compounds with scale.
The costs multiply in real-world scenarios:
Attention fragmentation: Every file requires context-switching. By the hundredth file, your attention is scattered and error-prone.
Inconsistent application: Early changes are careful. Late changes are rushed. The refactoring becomes inconsistent.
Testing burden: You need to verify changes across the entire affected surface area. Miss one test, and the bug ships.
Review difficulty: A PR touching 200 files is effectively unreviewable. Reviewers skim or rubber-stamp.
Worse, these refactoring tasks often get deferred. "We'll clean that up later" becomes technical debt that accumulates until the codebase becomes harder to work with.
How Devonair Approaches It
When you give Devonair a refactoring task, it doesn't just do find-and-replace. The agent takes a structured approach:
1. Codebase analysis
The agent first builds an understanding of your codebase structure - module boundaries, import patterns, naming conventions, and how code is organized. This context informs how it makes changes.
2. Reference identification
It identifies all references to the target, including:
- Direct function/variable references
- Dynamic references in strings
- Type definitions and interfaces
- Test files and mocks
- Documentation and comments
- Configuration files
3. Contextual decisions
For each reference, the agent makes contextual decisions. Should this comment be updated? Does this test name need to change to match? Is this string a coincidental match or an actual reference?
4. Validation
Before creating a PR, the agent runs your test suite and type checker to validate that changes don't break anything.
5. Reviewable output
Finally, it creates atomic, reviewable commits that explain the intent of each change. The PR shows exactly what changed and why.
Refactoring Prompts That Work Well
Be specific about what you want. Clear intent leads to better results.
Renaming:
@devonair rename the "getUserData" function to "fetchCurrentUser" across the entire codebase
@devonair rename the "utils" directory to "helpers" and update all imports
Pattern migrations:
@devonair migrate all class components in /src/components to functional components with hooks
@devonair convert all callback-based functions in /src/api to async/await
API updates:
@devonair update all calls to the v1 API endpoint to use the new v2 format
@devonair replace all usages of the deprecated "moment" library with "date-fns"
Type system changes:
@devonair convert all files in /src/utils from JavaScript to TypeScript, inferring types where possible
@devonair add explicit return types to all exported functions in /src/api
Best Practices for Large Refactors
Start with a single module
Before running a codebase-wide refactor, test the agent's understanding on a single module:
@devonair migrate class components to hooks, but only in /src/features/dashboard
Review the results. If the agent handled it correctly, expand the scope.
Be specific about intent
"Rename X to Y" is better than "clean up naming." The more specific your prompt, the more accurate the result.
@devonair rename all variables named "data" to more descriptive names based on their content
This is vague - what counts as "more descriptive"? Better:
@devonair rename "userData" to "currentUser" and "postData" to "blogPost" across the codebase
Review the PR carefully
AI is good but not perfect. Your review is essential. Check:
- Did the agent catch all references?
- Are the contextual decisions correct?
- Do the tests still pass?
- Does the code still make sense to a human reader?
Use scheduled tasks for ongoing maintenance
Don't let refactoring debt accumulate. Schedule regular cleanup:
@devonair schedule weekly: identify and remove any unused exports
@devonair schedule monthly: report on deprecated API usages that should be updated
Common Refactoring Scenarios
Here are real-world refactoring tasks that teams run through Devonair:
Framework migrations:
@devonair migrate from Express.js to Fastify in /src/server
@devonair convert all Redux state to Zustand stores
@devonair migrate from Styled Components to Tailwind CSS
Code modernization:
@devonair convert all var declarations to const/let as appropriate
@devonair replace all .then() chains with async/await syntax
@devonair update all array methods to use modern JavaScript (map, filter, reduce)
Consistency standardization:
@devonair ensure all React components follow the naming convention: PascalCase for components, camelCase for hooks
@devonair standardize all API error responses to use the ErrorResponse type
@devonair update all date handling to use the project's standard date-fns utilities
Each of these tasks would take a human developer hours of tedious find-and-fix work. With Devonair, they become a prompt and a PR review.
When Human Judgment Is Needed
Devonair excels at mechanical, pattern-based refactoring. It's less suited for:
- Architectural decisions: The agent can execute a migration, but deciding whether to migrate is a human call
- Ambiguous renames: If the new name isn't obvious from context, you need to specify it
- Business logic changes: Refactoring that changes behavior (not just structure) needs careful human oversight
- Performance-critical sections: Code where performance matters needs human review of the refactored version
- Security-sensitive code: Authentication, authorization, and encryption code deserves extra scrutiny
The best results come from clear, specific tasks where the intent is unambiguous.
The Refactoring Mindset Shift
Traditional refactoring requires you to choose between two bad options:
- Do it manually - tedious, error-prone, and nobody wants to
- Skip it - debt accumulates until it becomes a crisis
AI agents offer a third option: describe what you want, review what you get. The tedious part is automated. The judgment part stays with you.
This changes how teams think about refactoring. Instead of "we'll clean that up when we have time" (meaning never), it becomes "let's have Devonair clean that up." The barrier to refactoring drops from "days of tedious work" to "write a prompt, review a PR."
Teams that embrace this mindset keep their codebases cleaner because cleanup is no longer a sacrifice - it's just another task to delegate.
Getting Started
Pick a refactoring task you've been putting off. Something mechanical and tedious - exactly the kind of work that's easy to defer.
@devonair [describe your refactoring task]
Review the PR. If it looks good, merge it. If not, provide feedback and iterate.
That migration you've been avoiding for months? Describe it in a sentence and let Devonair handle the tedious parts.
FAQ
How does Devonair handle edge cases in refactoring?
The agent analyzes context to distinguish between actual references and coincidental matches. It identifies dynamic references, string templates, and comments that might need updating. Ambiguous cases are flagged for human review.
Can Devonair refactor across multiple repositories?
Currently, Devonair operates on one repository at a time. For monorepos, it can refactor across the entire codebase. Multi-repo refactoring is on the roadmap.
What if the refactoring breaks something?
Devonair runs your test suite before creating a PR. If tests fail, the agent attempts to fix them. If it can't, the PR includes information about what failed so you can address it during review.
How long do large refactors take?
It depends on the scope and complexity. Simple renames across a codebase happen quickly. Complex migrations like JavaScript-to-TypeScript take longer because the agent needs to analyze types, handle edge cases, and validate changes. Either way, it's faster than doing it manually.
Should I refactor everything at once or in stages?
For large changes, stages are usually better. Start with a single module or directory to validate the approach. Review the results. If the agent handled it well, expand the scope. This gives you checkpoints and makes code review manageable.
Can Devonair handle refactoring in legacy codebases?
Yes, but set expectations appropriately. Legacy code often has implicit dependencies, missing tests, and undocumented behavior. The agent will do its best, but you'll want to review more carefully and potentially add tests before major refactors.