Large-scale migrations are the highest-ROI application of autonomous coding agents. Why? They’re repetitive (the same pattern applied hundreds of times), well-defined (clear before/after states), verifiable (tests and type-checkers confirm correctness), and nobody wants to do them. A framework upgrade that touches 500 files is exactly the kind of work that sits in the backlog for months because no human wants to spend a week on mechanical changes.

Framework upgrades — React class components to hooks, Angular version bumps, Rails major versions. API deprecations — replacing deprecated method calls across a codebase. Security remediations — updating vulnerable dependencies and fixing breaking changes. Language migrations — JavaScript to TypeScript, Python 2 to 3. Monorepo restructuring — moving code between packages with import path updates.

Before writing a single line of code, the agent maps the entire migration surface. It scans the codebase for every instance of the pattern that needs to change — every deprecated API call, every class component, every usage of the old import path. It classifies each instance: straightforward (mechanical transformation), complex (requires understanding context), or needs human judgment (ambiguous intent, multiple valid approaches).

The output is a migration plan: 487 files need changes. 412 are straightforward transformations. 63 require context-aware changes. 12 need human review. This report is the first human review checkpoint. The developer validates the scope, confirms the classification, and approves the plan before any code is modified. This prevents the agent from making changes to files it shouldn’t touch.

A single PR that changes 500 files is unreviewable. The agent decomposes the migration into logical batches: by module, by directory, by dependency order, or by complexity level. Each batch becomes a separate PR that can be reviewed and merged independently. The key constraint: each batch must leave the codebase in a working state. You should be able to merge batch 1 without batch 2 and have everything still build and pass tests.

Some migrations have ordering constraints. If module A depends on module B, and both need changes, B must be migrated first. The agent builds a dependency graph and plans the batch order accordingly. This is where the hybrid approach shines — the dependency analysis is deterministic (AST parsing), while the actual code transformation is handled by the LLM.

Once the plan is approved, multiple agents execute in parallel — each handling one batch in its own isolated environment (git worktree or container). Agent 1 migrates the auth module, Agent 2 migrates the user module, Agent 3 migrates the billing module — all simultaneously. Each agent runs the relevant tests after making changes, iterates on failures, and opens a PR when its batch is complete.

The most effective migration tools combine AST-based codemods with LLM intelligence. Deterministic transformations (renaming imports, updating method signatures) use AST manipulation — fast, reliable, no hallucination risk. Complex transformations (restructuring logic, handling edge cases) use the LLM. This hybrid approach, used by tools like Codemod 2.0, gets the reliability of traditional codemods with the flexibility of AI for the hard cases.

Research on multi-agent refactoring (Jan 2026) identifies three specialized roles: (1) Scope Inference Agent — transforms the developer’s intent (“rename UserService to AccountService everywhere”) into an explicit refactoring scope (every file, import, reference, test, and documentation mention). (2) Planned Execution Agent — identifies all program elements needing changes and executes transformations via trusted APIs. (3) Replication Agent — handles project-wide search and ensures consistency.

A single generalist agent trying to handle scope analysis, code transformation, and consistency checking tends to lose context and make errors. Specialized agents are more reliable because each one has a narrower task with clearer success criteria. The scope agent doesn’t need to write code; the execution agent doesn’t need to understand intent. This separation of concerns mirrors how human teams handle large refactors.

Every migration batch must pass a verification stack before it’s ready for human review: (1) Type checking — does the code compile? Are there new type errors? (2) Test suite — do all existing tests pass? (3) Lint/format — does the code follow project conventions? (4) Behavioral equivalence — for refactors that shouldn’t change behavior, do the outputs match? (5) Migration-specific checks — are there any remaining instances of the old pattern?

The most important migration-specific check: are there any remaining instances of the old pattern? If you’re migrating from deprecated API v1 to v2, the agent should verify that zero calls to v1 remain after all batches are merged. This is a simple grep-level check, but it’s the one that catches the files the agent missed or the edge cases it couldn’t handle.

The agent starts migrating files that weren’t in the plan, or makes “improvements” beyond the migration scope. Prevention: strict file lists in the plan, and a verification step that checks the agent only modified files in its assigned batch.

The agent changes the code structure correctly but subtly alters behavior. A function that used to return null now returns undefined. Tests pass because they don’t check for this distinction. Prevention: behavioral equivalence tests and mutation testing on the migrated code.

Parallel agents modify files that overlap, creating merge conflicts when PRs are combined. Prevention: the decomposition phase must ensure batches have non-overlapping file sets. When overlap is unavoidable, those files go into a sequential batch.

Choose something with these properties: mechanical transformation (not judgment-heavy), good test coverage (you’ll know if the agent breaks something), limited scope (50–100 files, not 5,000), and low business risk (not the payment processing module). Dependency version bumps and deprecated API replacements are ideal first migrations.

Your first agent-driven migration teaches you: how to write effective migration specs, how long the analyze-plan-execute cycle takes, what your review process looks like for agent-generated PRs, and where the agent struggles with your specific codebase. This knowledge is more valuable than the migration itself — it’s the foundation for scaling to larger migrations.