Teams often underestimate how much context the model needs to make safe changes across many files. The common mistake is assuming the assistant can compensate for unclear naming, heavy abstraction, scattered dependencies, and weak documentation. That usually creates large pull requests that are hard to review. Smaller files, explicit comments, and typed interfaces make the work more controllable.
Why AI Assistants Struggle With Refactors Across a Large Codebase
The mistake is treating refactoring as a local text-edit problem when it is really a system-change problem. An AI assistant can rewrite code, but it cannot reliably infer all the hidden contracts, call paths, and build assumptions that make a large repository behave safely. That is why shallow prompts often produce technically valid changes that are architecturally brittle.
Refactors across many files depend on dependency awareness, naming consistency, and interface stability. If the codebase already has weak boundaries, the model is forced to guess which abstractions are real, which are accidental, and which usage sites will break when a symbol, type, or function signature changes. In practice, the assistant becomes more useful when the codebase is already easier for humans to reason about.
That is also why teams should not measure success by whether the tool can “handle” complexity. Smaller units of change, explicit comments, typed interfaces, and clearer module boundaries reduce the chance that an edit will be locally correct but globally wrong. The tool benefits from the same engineering discipline that makes human review effective.
Where Refactoring Work Breaks Down in Practice
The main failure mode is incomplete context. A large codebase contains patterns that are not obvious from a single file, including indirect dependencies, duplicated business logic, generated code, test fixtures, environment-specific conditionals, and framework conventions. If the assistant does not see enough of that surrounding structure, it may make changes that look consistent in isolation but violate an assumption elsewhere.
Another common error is overtrusting the assistant to preserve meaning during broad API changes. Renaming a method, moving logic across modules, or normalising data structures can ripple into serialization, validation, tests, and runtime behaviour. When teams ask for a broad refactor without constraining the blast radius, they often get a large patch that is difficult to verify because every file becomes part of the trust chain.
Large pull requests are not just a review burden, they are a control problem. Reviewers lose the ability to reason about intent, the assistant’s edits are harder to diff against the original design, and regressions hide inside apparently mechanical transformations. The best signal that a refactor is becoming unsafe is when the change stops being easy to explain in one sentence per module.
Practitioner Guidance for Safer AI-Assisted Refactoring
What to prioritise: Constrain the assistant to one architectural slice at a time. A good refactor request names the exact module boundary, the target interface, and the invariants that must not change.
What to verify: Check whether the codebase makes dependencies explicit enough for the model to reason about them. Strong types, narrow interfaces, and representative tests are better guardrails than vague instructions to “clean up” or “simplify” the code.
Common mistake: Treating the assistant as a substitute for design clarity. If the refactor depends on humans understanding which behaviour is authoritative, the repository still needs that knowledge encoded before the tool can help at scale.
Practitioner takeaway: Use AI assistants to accelerate well-bounded change, not to compensate for architectural ambiguity. The less the codebase tells the tool about intent, the more likely it is to produce a refactor that is syntactically correct but operationally risky.
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 address the attack and risk surface, while NIST CSF 2.0 and CIS Controls v8 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A3 — Tool Misuse and Privilege Abuse | AI assistants can make broad code changes that affect tool-like execution paths and trust boundaries. |
| Recommendation — Constrain AI-assisted refactors to bounded scopes and verify each changed path against the intended authority. | ||
| NIST CSF 2.0 | PR.IP — Information Protection Processes and Procedures | Refactoring quality depends on disciplined change procedures, testing, and change control. |
| Recommendation — Apply controlled change procedures and validation steps before merging large refactors. | ||
| CIS Controls v8 | 16 — Application Software Security | Large refactors need secure coding, review, and testing practices to avoid introducing defects. |
| Recommendation — Review refactored code with application-security checks and regression tests before release. | ||
Related resources from NHI Mgmt Group
- What do teams get wrong when they treat AI assistants as infrastructure?
- What do teams get wrong when they try to enforce secure API changes across large codebases?
- What do teams get wrong when they try to use one global role model across all tenants?
- What do teams get wrong when they try to use a RAG framework as a full agent orchestration layer?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on September 18, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
