Quick wins for a faster PC:
Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →An AI-assisted code change can solve its assigned task and still make the system harder to understand. The risk is cumulative: a helper, dependency, retry, or abstraction may look reasonable on its own, while repeated choices blur ownership and make future changes more difficult. Reviewing the immediate behavior is necessary; it is not the same as reviewing the architecture.
How a reasonable change can make the system worse
Robert Adamson’s September 29, 2026 essay on DEV Community frames this as a risk of accumulation, not a measured rate of AI-caused architectural decline. Its examples illustrate how a series of locally defensible changes can shift responsibilities, add dependencies, or make boundaries harder to explain. The essay does not establish how often this happens or prove that AI is the cause.
As an Amazon Associate I earn from qualifying purchases.
One example is a helper that gradually becomes a shared home for business logic. Each call site may appear to benefit from reuse, but the helper can end up owning rules that belong to distinct parts of the system. In another scenario, small changes create unclear ownership and dependencies in both directions. The concern is not that helpers or dependencies are inherently bad; it is that repeated decisions can change the system’s shape without anyone deliberately choosing that shape.
As Adamson puts it, “A system can get worse even when every local decision looks correct.” That is an engineering argument about the difference between evaluating one change and evaluating its cumulative effects, not a finding from a controlled study.
#1 Best Overall
Task correctness and architecture are separate review questions
A change may meet its request and pass its tests while leaving harder questions unanswered: Who owns this rule? Does this dependency point in the intended direction? Is the same business logic now expressed in multiple places? Will the next person know where to make a related change?
Tests can reveal behavioral regressions, but passing tests alone do not establish that ownership is clear or that dependency boundaries remain healthy. In Adamson’s essay, “100% tests passing” is an illustrative scenario, not a reported measurement or a guarantee of architectural quality.
Rank #2
This distinction applies whether a person or an AI agent wrote the change. Adamson’s formulation is: “The agent owns the task. You still own the architecture.” Treat the proposed implementation as something to evaluate, not as a transfer of responsibility for system design.
Free tools Windows power users keep installed
One-click scans. No signup required.
Ask what happens if the pattern is repeated
A useful review question from the essay is: “If we repeat this pattern 20 times, what does the system look like?” The number is a thought experiment, not a threshold or a prediction. Its purpose is to make the reviewer consider the direction a choice would take if it became routine.
Rank #3
Apply that question to the change’s concrete decisions:
- Abstraction: Does this abstraction clarify a stable responsibility, or could it become a catch-all for unrelated rules?
- Ownership: Is business logic staying with the part of the system responsible for it, or moving across a boundary without an explicit reason?
- Dependencies: Does the new dependency preserve the intended direction of responsibility, or introduce a connection that future changes will have to work around?
- Duplication: Is a rule being duplicated, or is an apparent duplication actually owned by different domains?
- Understandability: If this approach were repeated, would the system be easier to explain and change?
Adamson also describes a six-week progression as an example. It should not be read as a measured timeline for architectural decay. The broader point is to notice patterns across changes, rather than judging each pull request as if it were isolated.
Rank #4
Review boundaries before implementation
The essay proposes making architectural expectations explicit before work begins. Write down the relevant invariants—such as which part owns a business rule or which way dependencies should point—and ask for the architecture impact of a proposed change before implementation. These are suggested practices, not experimentally validated guarantees.
Windows Errors? Fix Them Before They Spread
Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallOutdated Drivers Are Slowing You Down
One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchAfter implementation, review the change at two levels: first, whether it satisfies the task; second, whether its abstractions, ownership, and dependencies fit the system’s intended boundaries. When a proposed helper or shared service would relocate business rules, ask whether that is an intentional design decision rather than accepting it as an incidental consequence of reuse.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use AI to look for drift, not to certify architecture
AI can help inspect a codebase for possible repeated patterns or boundary changes. The essay’s practical framing is to detect and rank possible findings before refactoring. A model’s observation is a prompt for human review, not proof that a pattern is harmful or that a suggested fix is sound.
That caution matters because a finding needs context: similar-looking code may belong to different domains, and a dependency that appears unusual may reflect an intentional boundary. Verify observations against the code and the team’s stated invariants before changing responsibilities or introducing a new abstraction.
A separate Programmer.ie chapter, “Trajectory Search,” discusses agents proceeding competently along a mistaken path and the importance of environmental evidence and independent verification. That is adjacent advice about agent evaluation; it does not provide direct evidence for claims about architecture drift. Read the chapter.
What the evidence does—and does not—show
Adamson’s DEV Community essay supplies scenarios and review advice, not an empirical study. It reports no named statistic establishing how common architecture drift is, how much AI contributes to it, or whether the proposed review practices reduce it. Its “20 times,” “100% tests passing,” and six-week references are examples, not measured findings.
For the full argument and its examples, see Robert Adamson’s essay on DEV Community.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
