How to Improve Architecture With a Refactor Agent

Architecture improvement through refactoring is the work of reducing codebase friction without changing observable behavior. The target is often a shallow module: an interface that is almost as complex as its implementation, spreads knowledge across callers, and makes future changes harder than they need to be.

The valuable refactor is not the broad cleanup. It is the small, behavior-preserving change that deepens one module, improves locality, keeps tests green, and ends as a reviewable pull request.

Why architecture drift quietly costs you

Technical debt rarely appears as one dramatic failure. It shows up as repeated hesitation. A developer has to read five files to understand one concept. A test has to reach past the public interface. A helper exists only because it was easier to test than the real behavior. A term in code means something different from the product glossary.

Those costs compound because the code still works. Without a disciplined refactor process, teams either avoid the area or attempt a large rewrite. Both paths are expensive. The safer pattern is to find the smallest deepening opportunity, pin current behavior, move in tiny commits, and stop when the interface is better.

What the manual process looks like

Done by hand, architecture improvement is a slow engineering review:

1. Read the target area, surrounding modules, tests, glossary, and recorded decisions.
2. Identify friction: shallow modules, leaked concepts, caller complexity, fuzzy domain terms, and hard-to-test behavior.
3. Apply the deletion test. If deleting a module makes complexity vanish, it may be pass-through code. If the complexity reappears across callers, the module may be earning its place.
4. Scope the refactor with the smell, target interface, out-of-scope boundaries, and behavior-preservation rule.
5. Check test coverage before moving code. Add characterization tests first when coverage is thin.
6. Refactor in the smallest working commits, running relevant tests after each step.
7. Draft the pull request with problem, approach, decisions, testing, and explicit confirmation that behavior did not change.

The discipline is in what the refactor refuses to do. If the work changes behavior, expands scope, or contradicts an existing architecture decision without approval, it is no longer a safe refactor.

What an agent can automate

The refactor and architecture improver gives that disciplined loop to a coding agent:

• Find the deepening opportunity. The agent reads the target area and uses the deletion test to identify the shallowest useful module.
• Use the project's language. It names boundaries and seams in domain terms rather than generic technical labels.
• Plan tiny commits. The agent writes a step-by-step refactor plan where each commit leaves the program working.
• Pin behavior before movement. If coverage is thin, early commits add characterization tests before structural changes.
• Apply behind tests. The agent runs the relevant test command after each step and stops instead of proceeding on red.
• Draft the PR. The final artifact explains the problem, approach, commit plan, decisions, tests, out-of-scope items, and behavior-preservation claim.

The agent does not merge the work. It ends at a human approval gate.

The guardrails that make it safe

Refactoring needs stricter guardrails than feature work because the success condition is absence of behavior change. The bundle makes that explicit in the setup fields: refactor goal, do-not-change boundaries, and verification command.

Task Machine also keeps the workflow staged. The agent first finds the opportunity, then plans tiny commits, then applies behind tests, then verifies behavior unchanged, then drafts the PR, then waits for approval. If a step gets bigger than planned, if tests go red, or if behavior has to move, the agent stops.

Set it up in Task Machine

The Refactor & architecture improver playbook installs the refactoring agent, workflow, debt-reduction goal, and skills for architecture improvement, refactor planning, and domain modeling. Setup takes a few minutes. You need a Task Machine workspace and permission to install playbooks (workspace owners have it). A connected repository is expected for full execution. Until repository access is connected, the agent works from attached code and drafts the PR for you to open.

1. Find the playbook

Open Playbooks in your workspace and search for "refactor architecture", or browse the Engineering category. The card describes the behavior-preserving refactor flow and the PR approval boundary.

2. Preview what it installs

Preview & install opens the full contents before anything is created: the Refactoring Agent, Refactor workflow, debt-reduction goal, and the three skills that guide deep module design, tiny-commit planning, and domain modeling.

3. Give the agent the refactor context

Start setup asks for the repository, refactor goal, do-not-change boundaries, and verification command. The goal should name the messy area and the architectural smell. The boundaries should name behavior, files, APIs, or product surfaces that must not move.

4. Generate and review

Generate customized playbook writes your context into the agent and workflow. Review before creation. Confirm that the workflow requires tests after each step, that observable behavior stays unchanged, and that the approval gate comes after the PR draft.

5. Install

Install customized playbook creates the agent, workflow, goal, and skills. A follow-up lands in your inbox to start the Refactor workflow. The first run confirms the messy area, behavior-preservation boundary, test command, tiny-commit plan, and PR handoff before the agent starts changing code.

What good looks like

A refactor run is healthy when three things are true:

• The interface is deeper. Callers need to know less, while more behavior lives behind the module boundary.
• The commits are small and green. Each step leaves the program working and the relevant tests passing.
• Behavior is unchanged. The PR can explain what moved structurally and what did not change for users or callers.

Common questions

How is this different from asking an agent to clean up code? The playbook is scoped to one target area, one behavior-preserving goal, and one reviewed PR. It does not run a broad cleanup.

What if tests are missing? The agent should add characterization tests first to pin current behavior. Refactoring without a behavior check is outside the safe path.

Can the agent change product behavior during the refactor? No. If behavior needs to change, the agent should flag that the work is no longer a refactor and ask for human direction.

Does it need repository access? Repository access is expected for the full workflow because the agent needs code, tests, and PR context. Without it, the agent works from supplied attachments and drafts the PR for a human to open.