dooopSoftware · Process · 12 min

How to Limit the Scope of AI-Generated Changes

Define boundaries before the prompt, separate fixes from refactoring, and review AI diffs with clear criteria to avoid out-of-scope changes.

Published on September 6, 2026

MAIN THESIS

AI easily expands diffs. Good review starts with scope boundaries.

Separating fixes, necessary adjustments, and refactoring prevents pull requests from turning into side negotiations.

Limiting the scope of changes with artificial intelligence requires an explicit authorization boundary. It is necessary to specify which behavior should change, what should remain the same, and what kind of intervention can be included in the diff.

The requested fix resolves a defect or a small verifiable change. Opportunistic refactoring reorganizes code without direct relation to that result.

The point is not to prohibit refactoring. It is to prevent a simple fix from arriving at review mixed with a reform that no one decided to make.

When AI Turns a Fix into a Reform

An AI-generated change often seems efficient when it delivers more than was requested. The assistant finds a poorly named function, notices duplication, suggests extracting a service, changes a convention, and ultimately opens a pull request larger than the original task.

At first glance, this may sound like technical zeal. In practice, it can hinder the simplest review decision: does this diff solve the requested problem with acceptable risk?

The problem is not AI suggesting improvements. In the case of GitHub Copilot Agents, the documentation describes agents with distinct environments and permissions and emphasizes human supervision and output review. The problem arises when the team does not define a boundary between fixing what was requested and taking the opportunity to tidy up.

This boundary needs to be explicit because human review does not only assess whether the code compiles. It evaluates intention, risk, reversibility, and adherence to the authorized change. A refactoring can be good and still be in the wrong pull request.

This distinction is especially useful for technical leadership already redesigning engineering practices with AI. Instead of discussing adoption all at once, it is worth focusing on a small, recurring process point: the real size of the change arriving for review. This focus connects to a broader discussion about how to create an AI strategy connected to the business, but here the decision is operational: accept, request division, or reject an out-of-scope change.

Define the Unit of Work Before the Prompt

Scope does not originate in the pull request. It originates in task formulation.

Before asking AI to change code, the team must transform the request into a small, independent, and testable unit of work. DORA recommends working in small batches to get feedback on changes and review hypotheses earlier. The same guidance warns about the difficulty of reviewing and integrating large AI-generated changes.

Applied to AI use, this changes the quality of the instruction. A prompt like “fix the discount calculation” leaves too much room. A more reviewable request would specify:

which behavior is wrong;
which expected behavior must be preserved;
which files are likely involved in the fix;
which existing tests must continue passing;
what type of change is authorized;
what should only be commented as a suggestion, without entering the main diff.

This boundary defines what can enter the diff and what should become a separate suggestion.

If the task authorizes only fixing a rounding, AI should not rename classes, reorganize directories, or change global conventions. If during execution it finds relevant duplication, that can become a comment, issue, or separate proposal. It does not need to be part of the same change.

This care is also a practical form of context engineering. Anthropic defines context engineering as selecting and maintaining information available to the model during inference, including instructions, tools, external data, and history, within a limited window. In a code task, context is not dumping the entire repository into the assistant. It is choosing which information helps solve that change without unduly expanding the scope.

Separate Three Types of Change in the Pull Request

A code review with AI becomes more objective when the team classifies each part of the diff into three groups.

The first group is the functional fix. This is the change that directly alters the requested behavior. If there was a defect in calculation, incomplete validation, or poorly handled error condition, this is the core of the task.

The second group is the necessary adjustment to enable the fix. Sometimes, to fix the behavior, it is necessary to change a test, adapt a helper function, adjust a configuration, or make explicit a dependency that was implicit. This group can remain in the same pull request as long as its relation to the fix is clear.

The third group is unsolicited refactoring. It may improve names, reduce duplication, organize layers, or simplify structures. But if it is not necessary to validate the requested change, it does not automatically belong to the same scope.

The review rule is: accept the first two categories when justified. Block or separate the third when it depends on a decision that was not made.

This avoids a common mistake: discussing technical taste as if it were a requirement. The question stops being “did the code get better?” and becomes “was this change necessary to solve the request with proportional review?”

Refactoring is not an error. Mixing independent refactoring with a small fix creates noise. When everything enters the same diff, the reviewer must validate behavior, architecture, naming, and intention simultaneously. The review ceases to be a decision about one change and becomes a negotiation about several.

Use the Diff to Ask: Does This Change the Outcome or Only Improve Form?

The diff is the best place to separate solution from preference. It shows what really changed, not just what the instruction promised.

When reviewing an AI-assisted pull request, some questions help maintain the boundary:

Does this file participate in the likely path of the defect or functionality?
Does this change alter the requested outcome or only improve internal form?
Does this rename help test the original hypothesis or just increase the review surface?
Does this reorganization change execution flow, public contract, or module dependencies?
Would the new test fail before the fix and pass after, or does it cover a side improvement?
Does the assistant’s or developer’s explanation justify each structural change?
If we need to revert the fix, can we do so without undoing a parallel refactoring?

These questions shift the discussion from technical preference to connection with the initial hypothesis.

They also help avoid a specific temptation: a change too elegant for the problem. AI may propose a more elegant, generic, or pattern-aligned solution. That is not enough. In engineering, elegance at the wrong time is also a cost.

The concrete criterion is the relation to the initial hypothesis. If the task was to fix an observable behavior, the diff should help demonstrate that this behavior changed as expected and the rest remained stable. What does not help this validation should be treated as a separate suggestion.

For organizations maturing their AI governance, this type of question also prevents the tool from becoming an excuse for implicit decisions. Maturity is not letting AI do more. It is knowing when to authorize, when to restrict, and when to divide. This perspective connects to the capability diagnosis discussed in AI maturity, but here it appears in a daily practice: reviewing a diff with a clear boundary.

Turn Found Improvements into Separate Tasks

Good opportunities should not be lost because they are out of scope. They just should not contaminate the main change.

When AI finds a real improvement, the team can record it in three ways:

comment on the pull request, indicating the improvement was noticed but not included;
backlog item, linked to the affected code;
separate refactoring proposal, with its own goal, risk, and acceptance criteria.

This habit protects two things simultaneously. It protects the current fix, which remains small and verifiable. And it protects the future refactoring, which ceases to be a side effect and becomes a deliberate decision.

A well-conducted refactoring deserves its own space. It may require architectural reading, additional tests, convention discussion, impact analysis, and alignment with other fronts. Hiding it inside a fix reduces decision quality.

There is a phrase worth using in review: “this may be a good change, but it is not this change.”

This phrase separates merit from opportunity. It recognizes the suggestion’s value without allowing the pull request to change nature midway.

Define What Stops the Review

Not every scope divergence needs to block a review. Some can be resolved with a comment, additional explanation, or file removal. But certain signals should make the team stop.

Review should be halted when:

build or automated tests break;
there is a change in public contract without explicit authorization;
files unrelated to the task were modified;
AI changed style, names, or structure in multiple parts without connection to the fix;
the pull request mixes defect fix with architectural reorganization;
the explanation does not connect structural changes to the requested behavior;
reverting the fix would also require reverting an independent refactoring.

DORA describes continuous integration as frequent integration into the main codebase, accompanied by build and automated tests. It also states that fixing a broken build should have priority over new changes. In an AI-assisted flow, this reinforces a simple rule: if the pull request increases basic uncertainty, priority shifts from adding more changes to restoring a verifiable base.

Stopping the review is not punishing the tool or developer. It is preserving the team’s ability to understand what is changing.

The more independent changes enter the same batch, the harder it is to assign cause when something fails. The team must investigate whether the problem came from the fix, refactoring, rename, new test, configuration, or their combination. This is an avoidable cost.

Signs That the Pull Request Went Out of Scope

Use these signs in reviews of AI-generated or altered code. They do not replace technical judgment but help create a common language for review.

Does the change directly alter the requested behavior? It is in scope when it fixes the defect, implements the requested requirement, or adjusts code necessary for that result.
Does the change only improve form, generality, or style preference? It is out of scope when not necessary to validate the requested behavior.
Is the changed file on the likely path of the defect or functionality? It is in scope when it participates in execution, validation, or configuration of the requested change.
Was the file touched by opportunity, broad standardization, or reorganization without demonstrable connection? It is out of scope.
Does the change make the fix easier to verify without spreading impact? It is in scope.
Does the change require reviewing architecture, naming, contracts, or execution flow not part of the task? It is out of scope.
Does the new or changed test prove the initial hypothesis? It is in scope when it fails before the fix and passes after, or clearly covers the requested behavior.
Does the test cover generic improvements, internal reorganizations, or unrelated scenarios? It is out of scope.
Does the AI’s or developer’s explanation connect each structural change to the requested fix? It is in scope.
Is the justification only general quality, future cleanup, or taking advantage of the moment? It is out of scope.
Can the change be reverted without undoing independent refactorings? It is in scope.
Are fix and refactoring mixed to the point of preventing a clean revert? It is out of scope.

The checklist’s usefulness lies less in producing a mechanical answer and more in reducing ambiguity. When the team disagrees, they discuss a visible criterion, not a feeling about pull request size.

Fictional Example: Fixing Discount Calculation Without Redesigning the Module

Imagine a fictional case in an e-commerce platform. The task is to fix rounding in progressive discounts. In certain quantity and percentage combinations, the displayed total differs from the total calculated at order checkout.

The instruction to AI says: fix the rounding, preserve current discount rules, change only files directly related to calculation and corresponding tests, and record improvement suggestions without applying them.

The pull request arrives with three types of change.

First, it changes the function responsible for rounding values in discount calculation. Second, it adds a test reproducing the scenario where the difference appears. Third, it renames the discount policy class, extracts a new pricing service, and changes folder organization to separate calculation, presentation, and persistence.

By this guide’s criteria, the first two items remain in the pull request. They connect directly to the requested behavior and make the fix verifiable. The third set should be removed. It may be a good refactoring but is not necessary to validate the initial hypothesis.

The review could note: “Extracting the pricing service may improve module organization but expands this fix’s scope. Remove from this pull request and open a separate suggestion with expected goal and impact.”

Nothing in this example claims that separation will automatically produce less rework or higher quality. Those effects would be hypotheses for the team to measure. What separation immediately allows is a more delimited decision: fix the rounding now, evaluate refactoring later.

This discipline also helps leadership see AI use as part of work design, not an isolated shortcut. If the organization is building an AI roadmap, it is worth including review practices, permission limits, and scope criteria among capabilities to develop. Choosing tools is not enough. It is necessary to decide how they enter the decision flow.

Before accepting an AI-generated or altered pull request, ask the team to classify the diff into functional fix, necessary adjustment, and unsolicited refactoring. Anything not connected to the requested behavior should be removed from the main pull request or become a separate suggestion.

If you want to discuss this decision in your company’s context, talk to dooop.

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