Design Reviews for Vibe-Coded Features: Using ADRs and Architecture Boards

You prompted an AI to build a feature. It worked instantly. The UI looks slick, the API responds in milliseconds, and you shipped it on Friday afternoon. But by Monday, your team is staring at a tangled mess of dependencies that no one understands. This is the trap of vibe coding: AI-assisted development where natural language prompts generate code rapidly, often bypassing traditional architectural planning. It feels like magic until the system starts rotting from the inside out.

The problem isn't that AI writes bad code. Modern models are surprisingly good at syntax. The real danger is that vibe coding produces working features that hide systemic dysfunction. Your feature might function perfectly in isolation, but when it interacts with the rest of your system, things break. AI doesn't see the forest; it only sees the branch you're typing on. To stop this decay, you need formal governance. Enter Architecture Decision Records (ADRs) and Architecture Boards. These aren't just bureaucratic hurdles-they are the brakes that keep your high-speed development train on the tracks.

Why Vibe Coding Breaks Architecture

When you rely on large language models (LLMs) to write code, you get speed, but you lose context. LLMs have limited memory windows. They don't know that your authentication service expects a JWT token in a specific header format unless you explicitly tell them every single time. More importantly, they don't understand the long-term implications of their choices.

A field report from Codecentric highlighted specific failures in unstructured vibe-coded projects. They found incorrectly implemented concurrency, strong coupling due to a lack of separation of concerns, and significant amounts of unused code fragments. Why does this happen? Because AI optimizes for the immediate prompt. If you ask for a user profile page, it might create a new database query method instead of reusing an existing repository pattern. It works, but it violates your architectural standards.

This reactive approach creates technical debt that compounds quickly. When tests are written after the fact-often by the same AI-they tend to expose previously hidden flaws, requiring multiple cycles of regeneration and patching. You end up with a codebase that functions but lacks cohesion. This is where design reviews become non-negotiable. You cannot afford to let architectural decisions happen implicitly through prompt engineering alone.

Architecture Decision Records as Guardrails

An Architecture Decision Record (ADR) is a lightweight document that captures a significant architectural choice, its context, and its consequences. Think of it as a commit message for your system's structure. In a vibe-coding workflow, ADRs serve two critical purposes: they force you to think before you prompt, and they provide context to the AI and future developers.

Before you ask an AI to implement a new payment integration, write an ADR. Define the constraints. Specify that you must use the existing Stripe library version. Mandate that all external calls go through your centralized HTTP client. Once this record exists, you can feed it into your AI tool's context window. Now, the AI isn't guessing; it's adhering to a spec.

Here’s how to adapt ADRs for AI workflows:

• Context: State clearly what part of the system is being modified and why.
• Decision: Describe the chosen approach. For example, "We will use event-driven communication for order processing."
• Consequences: List the trade-offs. "This increases latency but decouples services."
• AI Prompt Constraint: Add a section specifically for AI instructions. "Do not create new utility functions if one exists in `utils/http.js`."

Without this step, you’re letting the AI invent your architecture. As noted by Focused Chaos, "Even a rough system sketch is better than letting AI invent your architecture." An ADR turns that sketch into a binding contract.

The Role of the Architecture Board

If ADRs are the documents, the Architecture Board is the human element that enforces them. In many modern teams, the board has shifted from a gatekeeping committee to a collaborative forum. Their job isn't to say "no" to every new idea. It's to ensure that vibe-coded features align with the organization's long-term goals.

For vibe-coded features, the board’s review process needs to change. Traditional code reviews focus on line-by-line logic. Architecture boards should focus on integration points and systemic impact. When a developer submits a feature built via vibe coding, the board asks different questions:

1. Does this duplicate existing functionality? AI loves to reinvent the wheel because it doesn't know about the wheel you already have.
2. How does this interact with data persistence? Did the AI choose a NoSQL approach when your company standard is SQL?
3. Is the error handling consistent? AI-generated code often uses inconsistent try-catch blocks or ignores global error handlers.
4. What is the maintenance cost? Is the code readable, or did the AI generate clever but obscure one-liners?

This shift requires the board to be proactive. They shouldn't wait for a production outage. Instead, they should participate in the early design phase. Red Hat Developer notes that "when you vibe code, your instructions become obsolete the moment code is generated. The code itself becomes the only source of truth." The architecture board ensures that the code matches the intent, not just the prompt.

Integrating Design Reviews into the Dev Loop

How do you actually fit this into a fast-paced sprint? You don't want to slow down velocity, so the review process must be efficient. Here is a practical workflow for reviewing vibe-coded features:

Notice the emphasis on the "Pre-Prompt" phase. Most failures happen here. If you skip the ADR, you’re gambling. If you include it, you’re engineering. The peer review step is crucial because humans catch nuance that AI misses. For instance, an AI might write code that is technically correct but violates your team’s naming conventions or style guide. Humans fix that quickly. Architects fix the structural issues.

Supercharge Design recommends using vibe coding where it fits, then reinforcing it with reviews, tests, and clear structure. When security, data handling, or scale matters, bring engineering in early. Don’t let the AI decide how you handle PII (Personally Identifiable Information). That’s an architectural decision, not a coding task.

Common Pitfalls and How to Avoid Them

Teams adopting these practices often stumble over three main issues. First, they treat ADRs as optional paperwork. If an ADR isn’t reviewed, it’s useless. Make sure your CI/CD pipeline flags changes to core modules that lack an associated ADR update.

Second, they assume the AI understands legacy systems. It doesn’t. If you’re adding a feature to a ten-year-old monolith, the AI might suggest microservice-style patterns that clash with your monolithic transaction boundaries. The architecture board must bridge this gap by providing explicit examples of "good" code from the existing system to the AI’s context.

Third, they ignore the documentation drift. Code changes, but docs often lag. With vibe coding, the code changes faster than ever. Automate your documentation generation where possible, but keep the high-level architectural narrative in your ADRs. Those records explain *why* the system looks the way it does, which is invaluable for onboarding new hires who didn’t write the original prompts.

Maintaining Sanity in an AI-First World

Vibe coding isn’t going away. It’s becoming the default for prototyping and routine CRUD operations. But without governance, it leads to "shadow IT" within your own codebase-features that work but nobody wants to touch. ADRs and Architecture Boards provide the necessary friction. They slow you down just enough to prevent catastrophic acceleration.

Start small. Pick one upcoming feature. Require an ADR before the first prompt. Have a senior engineer review the output against that ADR. Measure the difference in refactoring time later. You’ll likely find that the upfront effort pays off in reduced debugging hours. Maintainability isn’t just about clean code; it’s about clean decisions. And in the age of AI, those decisions need to be recorded, reviewed, and respected.