How should teams use AI agents to start refactoring legacy code without breaking architecture?

Start by using AI agents to recover the architecture, map dependencies, and generate an initial test safety net before changing production code. Treat the first refactor as an experiment, not a commitment. Version control makes the work disposable, but independent verification is still required to confirm the change preserves the wider design and does not merely pass locally.

Why AI Agents Belong in the First Refactor Pass

AI agents are most useful at the beginning of a legacy refactor when the codebase is poorly documented, coupled, or partially understood. Their strongest value is not in making the first code change, but in accelerating architecture recovery, dependency mapping, and test discovery so the team can see the blast radius before touching production logic. That shifts the work from guesswork to controlled change.

In practice, this means treating the agent as an analysis and preparation assistant first. It can surface module boundaries, hidden assumptions, and likely integration points faster than manual review alone, but the team still owns the architectural interpretation. The output is only useful if it helps you decide what must stay stable while the refactor proceeds.

That is why the safest first use case is usually to generate a map of the current system, not a direct rewrite. If the agent cannot describe the dependency graph, entry points, side effects, and test gaps clearly, it is not ready to change code safely. The refactor should begin where the design is already visible enough to control.

How to Use Agents Without Letting Them Rewrite the Architecture by Accident

The core discipline is to constrain the agent to bounded tasks that preserve the existing system until the team has verification in place. Use it to summarize interfaces, identify fragile seams, suggest extraction candidates, and draft tests around current behavior. Do not give it unrestricted permission to reshape the design before the team has established the invariants that define the architecture.

This is especially important in legacy systems where local correctness can hide global damage. An agent may produce a patch that passes a narrow test set while weakening module boundaries, duplicating business rules, or creating new coupling. The right control is to compare the proposed change against the recovered architecture, not just against the diff itself.

For agentic code work, the most reliable pattern is iterative and disposable: establish a baseline, make a small change, verify behavior, and discard or revise quickly if the change breaks the intended structure. That makes version control part of the safety mechanism, but not the only one. Independent verification is what protects the wider design from well-formed but structurally harmful edits.

What Good Early Refactoring Looks Like in Practice

Early success is measured by improved observability of the codebase, not by the size of the rewrite. A good first pass leaves behind a clear dependency map, a test harness around critical behavior, and a smaller, better understood change surface. It should reduce uncertainty before it reduces code volume.

Teams should look for signs that the agent is helping them create safe refactor conditions: fewer unknown call paths, clearer ownership of modules, and tests that fail for the right reasons when the architecture is disturbed. If the agent only accelerates code generation without improving understanding, it is helping too late in the process.

In legacy systems, the smallest meaningful refactor is often a seam, not a feature. Extracting a function, isolating an adapter, or wrapping a brittle dependency can be a better first move than a broad cleanup. The architecture stays intact while the team learns where the real leverage points are.

Risk and Threat Considerations

AI-assisted refactoring creates risk when the agent is trusted to infer intent that the codebase does not make explicit. The failure mode is silent structural drift: the code still runs, but coupling, boundaries, or control flow no longer match the design the team thought it was preserving.

Failure mechanism: The agent generates changes from incomplete context, and the team validates only local behavior instead of architectural preservation, allowing hidden dependencies, duplicated logic, or contract breaks to enter the codebase.

Impact: Teams can ship a refactor that appears successful in tests while creating long-term maintenance debt, unstable integrations, and harder rollback decisions when the system is later extended.

Standards & Framework Alignment

This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.

OWASP Agentic AI Top 10 and CSA MAESTRO address the attack surface, NIST SP 800-53 Rev 5 and OWASP ASVS set the technical controls, and ISO/IEC 27001:2022 defines the regulatory obligations.

Framework	Control / Reference	Relevance
OWASP Agentic AI Top 10	ASI03 — Identity & Privilege Abuse	AI refactoring agents can overstep intended authority during code changes.
	ASI02 — Tool Misuse	The question is about safe agent use for refactoring and tool-driven code changes.
	ASI08 — Cascading Failures	A bad refactor can preserve local tests while breaking wider system behavior and dependencies.
Recommendation — Constrain agent actions to least-privilege tasks and verify every structural code change. Limit tool scope so agents can inspect and suggest without unrestricted modification rights. Stage refactors in small increments and validate upstream and downstream behavior after each change.
CSA MAESTRO	GRC — Governance, Risk and Compliance	Agent-led refactoring needs governance around acceptable autonomy and verification gates.
Recommendation — Define approval gates for agent-generated changes before allowing production-adjacent edits.
NIST SP 800-53 Rev 5	CM-3 — Configuration Change Control	Refactoring is a controlled change process that needs review and approval discipline.
	SA-10 — Developer Configuration Management	Legacy refactoring depends on controlled source and build changes with traceability.
Recommendation — Require change review and approval for structural refactors before merge. Track refactor changes in version control with clear baselines and rollback points.
OWASP ASVS	V15 — Secure Coding and Architecture	The page centers on preserving architecture while changing code with AI assistance.
	V16 — Security Logging and Error Handling	Verification and regression detection depend on observable failure signals during refactoring.
Recommendation — Validate that refactors preserve architectural constraints and do not introduce new coupling. Use logging and test failures to detect when refactoring alters behavior outside the intended scope.
ISO/IEC 27001:2022	A.8.32 — Change management	AI-assisted refactoring is a change-management problem with architecture-preservation risk.
	A.8.29 — Security testing in development and acceptance	The answer emphasizes test safety nets and independent verification before commit.
Recommendation — Apply formal change control to refactors that affect core modules or dependencies. Add regression and acceptance tests before allowing AI-generated refactor changes to land.

Practitioner Guidance

What to prioritize: Start with dependency recovery and test coverage around the highest-risk paths, not with large-scale code generation. If the agent cannot explain the current system clearly, it should not be driving structural change.

What to verify: Check that the proposed refactor preserves module boundaries, interface contracts, and critical side effects, and validate the change against a broader test set than the one the agent used to justify it. A local pass is not enough when architecture is the thing being protected.

Practitioner takeaway: Use AI agents to reduce uncertainty before you use them to reduce code, because the first refactor succeeds when the team can prove the design still holds after the change.