How AI Coding Agents Handle Multi-File Refactors (With Examples)

Rename one function used in forty files, and you find out fast whether an AI coding agent actually understands your codebase or is just pattern-matching text. A codex multi-file refactor is not a single edit repeated forty times — it is a research problem, a planning problem, and a verification problem stacked on top of the actual code change. Get the order wrong and you end up with a half-renamed function, three broken imports, and a test suite that fails in ways that take longer to debug than the original refactor would have taken by hand. This article walks through how capable terminal-based coding agents actually approach this class of work, using a realistic rename-across-the-codebase scenario as a running example.

Why multi-file refactors break naive agents

A single-file edit is forgiving. The agent reads the file, makes the change, and the blast radius is contained to that file. Multi-file refactors remove that safety net. The moment a change spans more than one file, three new failure modes show up that don't exist in single-file work.

The first is incomplete coverage. If the agent finds 38 of 40 call sites, the codebase doesn't fail loudly — it fails at runtime, in production, three weeks later, when someone finally calls the two paths that still reference the old name. Static analysis tools catch some of this, but not all of it, especially in dynamically typed languages or in string-based references (config keys, route names, event names) that don't participate in the type system at all.

The second is partial application. An agent that edits files in an arbitrary order, without checkpoints, can leave a codebase in a broken intermediate state if it gets interrupted, hits a token limit, or makes a mistake halfway through. A human doing the same refactor commits incrementally and can always roll back to the last good state. An agent without that discipline just leaves the working tree half-migrated.

The third is false confidence from surface-level matching. A naive approach greps for the old function name and replaces every occurrence. This breaks the moment the same string appears in a different context — a variable named getUserData that has nothing to do with the getUserData function you're renaming, a comment referencing the old behavior, a test fixture with a coincidentally identical string. Text replacement without semantic context is how refactors introduce more bugs than they fix.

Capable agents avoid all three by treating the refactor as a pipeline: explore, plan, execute in verified steps, verify again at the end. That pipeline is the actual subject of this article.

Step one: explore before touching anything

The single most important discipline in agentic refactoring is refusing to edit before you've built a complete picture of the blast radius. This looks unglamorous compared to actually writing code, but it's where most of the risk in a multi-file refactor gets eliminated.

A capable agent starts a rename or migration task by running broad, then narrowing, searches — not blindly opening files it guesses might be relevant. The typical sequence looks like this:

1. Grep for the literal symbol across the whole repository, including test files, config files, and documentation, not just source directories that "seem relevant."
2. Grep for string-based references to the same concept — route names, dictionary keys, event names, GraphQL field names — because renames in dynamic codebases often hide behind strings rather than identifiers.
3. Check re-exports and aliasing — does the function get re-exported from a barrel file under a different name? Is it aliased on import (import { getUserData as fetchUser })?
4. Look at the type signature or definition site to understand what the function actually does, not just what it's called, so a rename doesn't accidentally get bundled with an unrelated refactor of behavior.

This is deliberately redundant. A single grep for the exact function name will miss re-exports, dynamic property access, and string literals used as keys in a registry pattern. Running multiple searches with different assumptions is cheap; missing a call site in production is not.

# Broad grep, case-sensitive, across the whole repo
grep -rn "getUserData" --include="*.ts" --include="*.tsx" .

# Catch string-based references (route configs, dynamic dispatch)
grep -rn "\"getUserData\"" --include="*.json" --include="*.ts" .

# Catch re-exports that might rename it on the way out
grep -rn "export.*getUserData" .

Only after this pass does the agent have an accurate count of files touched, and — just as important — a list of files it initially assumed were involved but actually aren't.

Step two: build an explicit plan before editing

Once the search results are in, the next discipline is turning that list into an ordered, explicit plan rather than jumping straight into edits. This is where an agent's behavior should start to resemble a senior engineer's PR description more than a script.

A good plan for a rename-style refactor typically includes:

• The definition site — where the function, type, or config key is actually declared, updated first since everything downstream depends on it existing under the new name.
• The call sites, grouped by directory or module, so related changes land together and are easy to review as a batch.
• The test files, updated in the same pass as the production code they cover, not as an afterthought.
• The documentation and comments, which are easy to forget and are exactly the kind of drift that makes a codebase harder to trust over time.
• A verification step after each batch — type-check or test run — before moving to the next batch.

The value of writing this plan down, even informally, is that it turns "did I get everything" into a checklist instead of a feeling. It also gives you a natural place to parallelize: independent batches of files (say, the API layer and the frontend components that consume it) can be worked in parallel without stepping on each other, as long as neither batch depends on the other's intermediate state.

Step three: edit file-by-file, verify between steps

This is the part that separates agents that produce reliable refactors from agents that produce fast-looking but fragile ones. The pattern is: change a small, coherent unit of files, then verify, then move on. Never batch the entire 40-file change into one commit-sized action with a single verification pass at the very end.

Concretely, this means:

• After updating the definition site and its immediate callers, run the type-checker or linter scoped to that directory if the tooling supports it. Fast, local feedback catches broken imports before they propagate.
• After updating a batch of call sites, re-run the relevant grep to confirm the old name no longer appears except where it's intentionally preserved (for example, a deprecated alias kept for backward compatibility).
• Keep a running diff of what's changed so far, so if something breaks in step 30 of 40, you know exactly what the last known-good state was and can revert just the last batch rather than the whole effort.

This incremental discipline matters more as the number of files grows. A 3-file change tolerates a single verification pass at the end because the search space for a bug is small. A 40-file change does not — if something breaks, you want to localize the failure to the last five files you touched, not scan all forty.

Worked example: renaming a widely-used function across a codebase

Let's make this concrete. Say a team has a function called getUserData scattered across a TypeScript monorepo — a backend service, a shared types package, and a React frontend — and they want to rename it to fetchUserProfile because the function's scope grew beyond "data" into a richer profile object, and the old name is now misleading to anyone reading the code fresh.

Exploration phase. The agent starts by grepping broadly, exactly as described above:

grep -rln "getUserData" packages/

Suppose this returns 40 files across three packages: packages/api, packages/shared-types, and packages/web. The agent doesn't stop there — it also checks for barrel exports and dynamic references:

grep -rn "export .* getUserData\|export { getUserData" packages/
grep -rn "\[.getUserData.\]\|routes\[.getUserData.\]" packages/

This second pass turns up something the first pass would have missed: packages/shared-types/index.ts re-exports the function under an alias, export { getUserData as fetchUser }, which is itself imported in four files under a completely different name. Those four files won't show up in a plain grep for getUserData — they show up as fetchUser. Missing this is exactly the kind of gap that causes a refactor to look complete and ship broken.

Planning phase. The agent lays out the batches:

1. packages/shared-types/src/user.ts — the definition site, plus its barrel re-export.
2. packages/api/** — 14 call sites, all first-party, straightforward rename.
3. packages/web/src/** — 22 call sites, including the 4 that import via the fetchUser alias, which need the alias updated too, not just the underlying name.
4. Test files across all three packages — 3 files with unit tests referencing the function name in test descriptions (describe("getUserData", ...)), which are cosmetic but worth fixing so the test output stays meaningful.

Execution phase. The agent updates the definition site first:

// packages/shared-types/src/user.ts
// before
export function getUserData(id: string): UserProfile { ... }

// after
export function fetchUserProfile(id: string): UserProfile { ... }

// packages/shared-types/index.ts
// before
export { getUserData as fetchUser } from "./src/user";

// after
export { fetchUserProfile } from "./src/user";

Note the second change removes the alias entirely rather than just renaming what it points to — because the alias existed to smooth over the old, misleading name, and now that the real name is clear, the alias is just extra indirection worth deleting. This is a judgment call an agent should surface in its plan rather than make silently, since it changes the public import surface of the shared package.

With the definition site updated, the agent moves to packages/api, doing a scoped find-and-replace followed by an immediate type-check:

grep -rln "getUserData" packages/api/ | xargs sed -i '' 's/getUserData/fetchUserProfile/g'
cd packages/api && npx tsc --noEmit

If the type-check passes, the agent moves to packages/web, handling the trickier alias-based imports explicitly rather than relying on a blind text substitution, since those four files import fetchUser, not getUserData:

// before
import { fetchUser } from "@app/shared-types";
const profile = fetchUser(userId);

// after
import { fetchUserProfile } from "@app/shared-types";
const profile = fetchUserProfile(userId);

After each package, the agent re-runs the grep to confirm zero remaining references to the old name outside of, say, a changelog entry that should legitimately keep the historical name for context:

grep -rn "getUserData" packages/ --include="*.ts" --include="*.tsx"

Verification phase. With all 40+ files updated, the agent runs the full test suite and a repo-wide type-check, not just the scoped ones it ran mid-flight:

npm run typecheck && npm test

A clean run here is the actual signal that the refactor is done — not the absence of grep hits, since grep only tells you the text is gone, not that the code still works.

Handling renames that aren't purely textual

The example above is a best case: the function name is a unique enough string that scoped find-and-replace is mostly safe once aliases are handled. Real refactors are often messier, and it's worth naming the messier cases explicitly, because "just grep and replace" quietly breaks on all of them.

Overloaded or common names. If you're renaming something called parse or validate, a plain text search returns hundreds of false positives — every other parse function in the codebase that has nothing to do with your target. The fix is to search with more context: the import statement that brings the specific function in, the exact module path, or the specific call signature, rather than the bare identifier.

Renames that cross language or serialization boundaries. A field name change in a backend model might also need to update a JSON schema, an OpenAPI spec, a GraphQL type, and a frontend TypeScript interface — four different syntaxes, none of which will be caught by a single grep pattern. Each surface needs its own search pattern and its own verification step (schema validation, GraphQL codegen, type-check).

Config-driven references. Feature flags, route names, and permission keys are frequently stored as plain strings in JSON or YAML config rather than as code identifiers. These don't get flagged by a type-checker at all if you miss one — the failure is silent until the config value is looked up at runtime and returns undefined. This is exactly why the exploration phase has to include a dedicated pass for quoted-string occurrences, not just identifier occurrences.

Database column or table renames. These deserve special caution because they're not reversible in the same way a code rename is — a bad migration on a production database can mean data loss, not just a build failure. A capable agent treats a schema rename as a two-step migration (add new column, backfill, dual-write, then drop old column) rather than a one-shot rename, and it should say so explicitly rather than silently applying a destructive ALTER TABLE RENAME COLUMN to a production-shaped migration file.

Tests and type-checks as the real safety net

Grep tells you where the old name still appears. It does not tell you whether the code still works. That's what tests and type-checks are for, and treating them as an integral part of the refactor — not a final "let's see if it passes" step — is what separates a controlled refactor from a hopeful one.

The practical pattern is to run the fastest, most scoped check available after each batch of changes, and the full suite at the end:

• Type-checking catches the largest category of rename bugs for free, in a statically typed language: a missed call site simply won't compile. This is the cheapest verification available and should run early and often, not just once at the end.
• Unit tests catch behavioral regressions that type-checking can't see — for instance, if the rename was bundled with a subtle signature change (a new required parameter, a different return type shape) that the agent should have called out separately.
• Integration or end-to-end tests catch the cross-boundary issues — the config-driven references and serialization mismatches described above — that neither grep nor the type-checker will find, because those tests actually exercise the runtime path.

An agent that skips this and just declares the refactor "done" once the grep count hits zero is optimizing for the wrong signal. Zero remaining references to the old name is necessary but nowhere near sufficient — it says nothing about whether the new references are wired correctly.

Committing in reviewable increments

A 40-file refactor dumped into a single commit is hard to review and hard to bisect if something breaks later. The better pattern — and one worth asking an agent to follow explicitly — is to commit in the same batches used for verification: definition site and shared package first, then each consuming package, then test and doc updates last.

This has two concrete benefits. First, a reviewer can actually read the diff in a sane order — new definition, then mechanical call-site updates, then cleanup — instead of scrolling through 40 files with no discernible structure. Second, if a regression surfaces days later, git bisect lands on a commit that touched five related files instead of all forty, which cuts debugging time significantly.

This also plays well with how agents should communicate progress on long-running refactors: a summary at each batch boundary ("updated definition and shared-types package, type-check clean, moving to API layer next") gives a human supervising the work a natural checkpoint to interrupt, redirect, or approve before the blast radius grows further.

When to parallelize instead of going file-by-file

Sequential, verified batches are the safe default, but they're not always the fastest path, and on a large enough migration — hundreds of files, several independent subsystems — going strictly sequential wastes time on work that doesn't actually depend on itself. If the API layer and the frontend layer don't share any files and both only depend on the already-updated shared-types package, there's no correctness reason to update them one after another rather than at the same time.

The catch is that parallel work on a shared codebase needs the same discipline as sequential work, just distributed: each parallel unit needs its own clear scope, its own verification step, and a merge point where the combined result gets checked together, not just each piece in isolation. Running five independent workers that each declare success on their own slice doesn't guarantee the whole migration is coherent once merged — that final integration check is not optional.

This is exactly the kind of problem our Claude Code Subagents course is built around: how to decompose a large migration into independently verifiable units of work, hand each unit to a separate subagent with a scoped context window, and reconcile the results without losing the verification discipline that makes a sequential refactor safe in the first place. If you've felt the pain of a monorepo-wide config migration or a rename that touched every service, that course walks through the orchestration patterns — not just the single-agent techniques — that make those jobs tractable instead of terrifying.

Closing thoughts

The mechanics of a codex multi-file refactor aren't exotic — grep before you edit, plan before you execute, verify after every batch, and treat tests and type-checks as the real source of truth rather than a clean-looking diff. What separates a reliable agent from a risky one is discipline in applying that loop consistently across dozens of files, especially on the messy cases — aliases, string-based references, cross-boundary renames — where a naive find-and-replace quietly does the wrong thing. Whether you're doing this by hand, with a single agent, or coordinating several agents in parallel on a larger migration, the underlying safety net is the same: know your blast radius before you touch anything, and never trust a refactor that hasn't been verified in the same increments it was built.