Agentic planning for cross-package refactors

Large codebases rarely live in a single package. When you need to rename a core interface, migrate to a new authentication library, or update API contracts across dozens of repositories, a manual, file-by-file approach consumes days of developer time and invites subtle bugs. Agentic planning—autonomous AI agents that plan, execute and validate changes across multiple packages—transforms these high-friction tasks into supervised workflows that complete in hours, not sprints.

This guide walks through how agentic systems coordinate cross-package refactors, the concrete planning steps they perform, and the tradeoffs between agent-driven automation and traditional scripted migrations. You'll see what makes distributed refactoring uniquely difficult, how agents map dependency graphs and generate changesets, and practical patterns for testing and rollback when changes span team boundaries.

Why cross-package refactors resist traditional automation

A refactor that touches ten files in one repository is straightforward: run sed, verify with a test suite, and commit. When those ten files live in five repositories owned by three teams, the problem multiplies. Each package may use different build systems, have distinct test harnesses, and depend on different versions of shared libraries. A naive find-and-replace breaks runtime contracts; a half-completed migration leaves the monorepo in an unbuildable state for hours.

Traditional scripts handle syntactic rewrites well but struggle with semantic dependencies. An agent that understands call graphs, type hierarchies and integration boundaries can propose changes that preserve invariants—renaming a method in package A, updating its callers in packages B and C, and adjusting mock implementations in test utilities—all in a single coherent plan.

Human oversight remains essential. Agents surface their proposed changeset for review before execution, letting engineers spot edge cases (legacy clients that still need the old API) or adjust scope (defer the database layer until next quarter). The value isn't full autonomy; it's collapsing discovery, planning and scaffolding from days into minutes.

How agents map the refactor surface

Effective agentic planning begins with dependency discovery. The agent queries version control APIs to enumerate repositories, parses go.mod, package.json, requirements.txt or equivalent manifests, and constructs a directed graph of which packages import which symbols. For example, if you're renaming AuthService to IdentityService, the agent identifies every package that imports the interface, every struct that embeds it, and every test file that mocks it.

Next comes impact analysis. The agent scans call sites to distinguish direct usage (a package instantiates AuthService) from transitive exposure (a public function returns AuthService as part of its signature). Direct usage requires code changes; transitive exposure may need API versioning or compatibility shims. By categorizing each reference, the agent builds a priority-ordered list: critical paths that will break builds, optional migrations that can wait, and deprecated code that can be deleted outright.

Finally, the agent generates a change plan grouped by repository and execution order. If package C depends on package B, which depends on package A, the agent sequences changes as A → B → C, ensuring each downstream package sees the updated API before its own refactor begins. The plan includes checkpoint tests—run the test suite in package A, verify it passes, then proceed—so failures halt the cascade early.

Platforms like Goatfied's BUILD environment provide AI coding agents that test their own changes and open pull requests on GitHub, Bitbucket or Azure DevOps, streamlining the review-and-merge cycle across multiple repositories.

Execution patterns: monorepo vs polyrepo strategies

In a monorepo, all packages share a single commit history. The agent can prepare a single pull request with coordinated changes across every affected directory, then rely on the unified test suite to validate the entire refactor atomically. Rollback is trivial—revert one commit—and deployment is simultaneous, eliminating version skew. The tradeoff is scale: very large monorepos may have slow CI pipelines, so agents must estimate test runtime and potentially split changes into dependent PRs that merge in sequence.

In a polyrepo setup, each package lives in its own repository with independent release cadences. The agent must open multiple pull requests, shepherd them through different approval workflows, and coordinate merge order to avoid breaking dependent services. A common pattern is to introduce backward-compatible changes first (add the new IdentityService interface alongside AuthService), migrate callers in dependent repos, then remove the old interface in a final cleanup PR. This "expand-contract" approach tolerates staggered merges but increases total changeset size.

Hybrid strategies combine both: the agent refactors a core monorepo, then auto-generates migration guides or codemods for external consumers. For example, after updating a public SDK in your monorepo, the agent produces a script that downstream teams run to upgrade their own code. This shifts some manual work to consumers but scales better when you have dozens of external clients.

Testing, validation and safe rollback

Agents should never merge untested changes. A robust workflow includes:

1. Unit tests in each modified package, run immediately after code generation.

2. Integration tests that exercise inter-package APIs, catching contract mismatches the unit layer misses.

3. Smoke tests in staging or preview environments, especially for polyrepo refactors where dependencies deploy separately.

If any test fails, the agent halts, attaches logs to the pull request, and flags the failure for human review. Engineers can revise the plan (skip a problematic package, adjust type mappings) and re-run. The agent applies fixes and re-tests until the suite goes green or the human decides to pause.

For rollback, monorepo refactors revert the single merged commit. Polyrepo migrations need a rollback plan per repository: either revert individual PRs in reverse dependency order, or maintain the old API alongside the new until all consumers migrate, then deprecate. Agents can generate rollback scripts as part of the initial plan, documenting the exact git commands or feature-flag toggles required to undo each step.

Governance and human-in-the-loop controls

Even the most capable agent should not auto-merge breaking changes across production services. Practical controls include:

• Approval gates: require a senior engineer to review the generated plan and each PR before merge.
• Diff preview: show a unified diff of all proposed changes before the agent writes any code, so reviewers catch scope creep early.
• Budget caps: limit agent runtime or API calls to prevent runaway executions that rack up compute costs.
• Audit trails: log every repository accessed, every file modified and every test result, ensuring compliance teams can reconstruct what happened and when.

Goatfied's GOAT agents let you set monthly budgets per agent, approve or revise their work, and track decisions in a shared audit log, providing centralized oversight without micromanaging every edit.

These controls turn agentic planning into a supervised workflow: the agent proposes, the human decides, and both share responsibility for outcomes.

Practical checklist for agent-driven refactors

• [ ] Map dependencies with a script or agent query to identify all affected packages.
• [ ] Define the refactor scope: which symbols to rename, which APIs to version, which deprecated code to delete.
• [ ] Choose monorepo or polyrepo strategy based on your repository layout and deployment model.
• [ ] Generate the change plan and review it for unexpected inclusions or missing edge cases.
• [ ] Set up checkpoint tests in each package to halt execution on failure.
• [ ] Run the agent in a feature branch or preview environment, not directly against main.
• [ ] Review diffs before merging, paying special attention to public API changes and test coverage.
• [ ] Document the rollback procedure and test it in a non-production environment.
• [ ] Monitor deployments for error spikes or latency regressions after the refactor lands.

Frequently asked questions

Can agentic planning handle refactors in languages with weak type systems?

Yes, but with caveats. Statically typed languages (Go, TypeScript, Rust) give agents precise call graphs and type constraints, reducing ambiguity. Dynamically typed languages (Python, JavaScript, Ruby) require runtime analysis or test coverage to infer usage patterns. Agents can still generate changesets by parsing import statements and searching for string literals, but they may miss metaprogramming edge cases (dynamic getattr calls, reflection-based factories). Pairing the agent with comprehensive integration tests mitigates this risk.

How do agents resolve merge conflicts when multiple refactors run concurrently?

Most agent workflows operate in isolated feature branches, so conflicts surface during pull request review, not during agent execution. If two agents modify the same file, the second PR will show a merge conflict that a human resolves by choosing one change, merging both, or re-running the second agent atop the first's result. Some platforms queue agent tasks to serialize conflicting refactors; others allow parallelism and rely on code review to reconcile differences.

What happens if the agent's plan is too large to execute in one session?

Large refactors can be split into phases. The agent outputs a sequence of smaller plans—phase one renames the interface in the core package, phase two updates five dependent packages, phase three updates the remaining ten—and each phase runs as a separate PR. This incremental approach keeps diffs reviewable, limits blast radius if something breaks, and lets teams merge and deploy at their own pace. The agent stores state (which packages are done, which are pending) so it can resume from the last checkpoint.

Do cross-package refactors require special permissions or infrastructure?

Agents need read access to all relevant repositories and write access to create branches and pull requests. In enterprises with strict permission models, this may involve service accounts, personal access tokens scoped to specific repos, or OAuth flows that let the agent act on behalf of a user. Audit logs should capture which agent performed which write, tying actions back to a responsible human approver for governance and compliance.

How do you measure whether an agent-driven refactor saved time?

Compare the wallclock hours from kickoff to final merge against a historical baseline for similar manual refactors. For example, if past three-repo migrations took 16 developer-hours and the agent plan took two hours of setup plus four hours of review, you've saved roughly ten hours. Track secondary metrics like defect rate (bugs introduced per line changed), test coverage delta, and rollback frequency to ensure speed gains don't come at the cost of quality.

Related reading

• Self-hosted AI coding assistants: a complete Goatfied deployment guide