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Yes—give architecture a safety net by making important decisions visible to coding agents and enforcing the parts that can be tested. Record the intent, add deterministic checks to the development pipeline, and reserve human review for changes where risk or judgment makes automation insufficient. The goal is safer change, not a design that can never evolve.
Why AI-generated code needs architectural guardrails
A coding agent does more than fill in code. While completing a task, it may choose a framework, data store, authentication approach, integration, or orchestration pattern. If the repository does not make its constraints clear, the agent may produce code that works locally but conflicts with the system’s intended structure.
Konrad and coauthors describe this kind of implicit decision-making as “vibe architecting” in their 2026 paper, Architecture Without Architects. Their demonstration is illustrative, not experimental proof that agents routinely cause architectural damage. The practical point is narrower: implementation prompts and agent behavior can shape architectural choices, so teams should not assume those choices are being made deliberately on their behalf.
That concern is part of a broader shift in software work, but productivity claims need care. Google SRE describes “up to a 4x increase in productivity” as an organizational target, not a measured result that every team should expect. Faster generation makes it more important to catch violations consistently; it does not prove that any particular guardrail improves quality or productivity by a specific amount.
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What an architectural safety net consists of
A practical safety net has three parts: context before generation, objective checks during development, and human judgment for changes that carry meaningful risk or cannot be evaluated mechanically.
| Control | What it does | Best fit | Limitation |
|---|---|---|---|
| Repository guidance and decision records | Explains constraints, rationale, and scope before an agent edits code. | Decisions that need to shape implementation choices. | Guidance steers behavior; it does not prove compliance. |
| Deterministic checks | Fails a build or development step when an explicit invariant is violated. | Rules that code, configuration, or dependencies make observable. | Checks can be incomplete, costly to maintain, or overly restrictive. |
| Model-assisted review | Surfaces possible issues that are difficult to express as simple rules. | Contextual review prompts and broad risk triage. | Its output is probabilistic, not a reliable pass/fail guarantee. |
| Human review | Assesses intent, trade-offs, and consequences in context. | Security-sensitive, production-impacting, or boundary-changing work. | Reviewer attention is limited; reviewing every change can become perfunctory. |
A useful distinction from the official AWS Security Blog is between author-time controls, build-time controls, and human review. As the article puts it, “The right balance between human review and automated speed depends on the risk profile of the change.”
Record decisions, then test the ones that can be tested
Use decision records to preserve intent
An architecture decision record (ADR) should capture the decision, its rationale, and the scope in which it applies. A short, current record is more useful to an agent and a reviewer than a vague instruction such as “follow the architecture.” Keep the decision reviewable so the team can change it deliberately rather than letting implementation drift redefine it by accident.
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The architecture-decision-record project documentation distinguishes the roles this way: “A decision record documents the decision, while a fitness function assures the decision.”
Turn objective invariants into fitness functions
A fitness function is an automated check that tests whether the system continues to meet an architectural constraint. For example, if an event-sourced system requires state changes to emit events, a test or policy can fail when a change path updates state without producing the required event. The ADR explains why that constraint exists; the fitness function checks code for a specific violation.
Not every decision is testable. “Keep the interface easy to understand” needs judgment unless the team can define observable criteria. Do not present a review prompt as a deterministic test: preserve subjective decisions as explicit questions for reviewers instead.
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Choose boundaries that are valuable and observable
Begin with a small set of rules that prevent consequential, recognizable violations. Useful candidates depend on the codebase, but may include:
- Module dependencies: which packages or services may depend on one another.
- Layering: whether domain logic can import presentation or infrastructure code.
- API conventions: required error formats, versioning rules, or compatibility constraints.
- Data access: which components may query a store or modify particular records.
- Security invariants: required authorization checks, secret handling, or approved cryptographic practices.
- Change scope: whether a task may alter shared interfaces, migrations, deployment configuration, or only a specified module.
Make each rule as precise as the evidence allows. A rule that cannot identify what it checks will either miss violations or block valid work. Assign an owner, document exceptions, and revisit checks when the architecture changes.
Example: Java package and layer rules
For Java projects, ArchUnit 1.5.1’s official user guide documents tests for package and class dependencies, layers, slices, and cycles. It is a language-specific option, not a universal architecture checker; teams should apply the same principle using tools compatible with their languages and repository structure.
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Put checks at the right points in the workflow
- Before generation: Put concise repository-level guidance where coding tools can see it. Link to the applicable ADRs and state the boundaries that matter for the task. Treat this as context, not enforcement.
- While editing: Run fast, local checks in the editor or through a pre-commit step where practical. Early feedback can help a developer or agent correct a dependency, layering, or formatting issue before it reaches review.
- At build and CI: Make the authoritative version of deterministic checks part of the normal test or build pipeline. A check that exists only in local instructions can be skipped; CI provides a consistent gate for changes that reach the shared branch.
- At deployment, when needed: Add a deployment-stage policy for constraints that can only be evaluated against release configuration or a target environment. Keep such checks tied to a concrete risk rather than adding another gate by default.
Archgate’s documentation describes a vendor example of connecting ADR context for agents with automated validation of changes and a learning loop. That illustrates one possible workflow, not independent evidence that a particular product or approach outperforms alternatives. The general pattern—make intent available, then validate observable rules—does not depend on a specific vendor.
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Require stronger review when a change crosses a boundary or has consequences that are difficult to reverse. This can include security-sensitive work, production-impacting changes, shared API or data-model changes, and unresolved design trade-offs. For a narrow, well-tested change that stays within established boundaries, let automation handle routine checks and avoid adding unnecessary approvals.
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Reviewers should receive a clear prompt tied to the decision at stake. Instead of asking whether a patch “looks architectural,” ask whether it introduces a new dependency across a prohibited boundary, changes the security model, or requires an ADR update. If the answer depends on context rather than a machine-verifiable rule, say what judgment is required.
Keep governance from freezing the architecture
Guardrails should protect decisions the team still endorses, not preserve every old choice indefinitely. When a check blocks a legitimate change, determine whether the implementation violates the rule, the rule is too broad, or the architecture decision should be revised. Record the exception or decision change so the system’s intended direction remains clear.
Repeated review findings and incidents are useful signals: they may point to missing guidance, an invariant that can be automated, or a decision that no longer fits. Remove or revise obsolete checks instead of teaching agents and developers to work around them. Lipsanen and coauthors’ 2026 Shift-Up paper explores structured requirements and architectural artifacts as guardrails, but reports preliminary exploratory findings—not broad validation that a particular tool improves productivity or quality.
For a deeper treatment of evolutionary design, fitness functions, automated architectural governance, and deployment-pipeline automation, O’Reilly’s listing for Building Evolutionary Architectures, 2nd Edition (published November 2022) includes chapters on those topics.
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