You’ve Generated Your MVP Using AI. What Does That Mean for Your Software Architecture?

The Black Box Problem in AI-Generated Architecture

"Any sufficiently advanced technology is indistinguishable from magic." - Arthur C. Clarke

AI's code generation capabilities can seem almost magical, but this magic comes with a fundamental architectural challenge: you can't make decisions about what you can't understand. When teams use AI to generate their Minimum Viable Architecture (MVA), they're implicitly accepting architectural decisions made by an opaque system.

The Three Core Challenges

When AI generates your MVP code, you face three interconnected architectural problems:

1. Loss of Control Over Architectural Decisions

While AI can generate impressive code, teams have no direct control over the architectural decisions embedded within it. You might be able to query the AI about some choices, but many decisions remain opaque because the AI doesn't understand the reasoning behind the patterns it learned from training data.

This mirrors the framework problem we've discussed before: frameworks make decisions for you, but you often don't know what they are. With AI-generated code, the opacity is even greater.

2. The Black Box Reality

From the development team's perspective, AI-generated code is largely a black box. Even if it could be understood, no one has time to do so. Teams are under intense time pressure, and the very act of using AI to relieve this pressure increases business expectations around productivity.

This creates a dangerous cycle: AI generates code quickly, but the architectural debt accumulates silently. Unlike traditional technical debt that can be repaid through refactoring, AI-generated code isn't meant to be maintained—it can only be replaced by more AI-generated code.

3. Interface and Integration Challenges

AI-generated code must interface with existing systems through APIs, and teams need to ensure that Quality Attribute Requirements (QARs) for the entire system-of-systems are still met. For example, if AI-generated code must satisfy security requirements when interfacing with existing systems, how do you verify this when you can't see the underlying decisions?

The Shift from Design to Empirical Validation

When you can't understand the architecture, you must validate it through experimentation. This represents a fundamental shift in how software architecture works.

Three Critical Questions Remain

The core questions teams need to answer about their architecture don't change with AI:

1. Is the product worth building? AI can help test an MVP with customers sooner and with less effort
2. Can it perform and scale? AI can help arrive at designs that can be empirically tested, but if it doesn't scale, the team must generate something else
3. What about lifetime cost? This is where AI-generated solutions become most problematic

The New Architectural Focus

AI shifts architectural focus from deciding how the system will work to how the system's architectural qualities will be validated. The art of architecting becomes knowing which QARs most affect the system architecture and focusing empirical testing there.

Teams need to develop new skills and insights to meet this challenge. Traditional techniques like architectural design reviews, code reviews, and security reviews become impractical given the volume of AI-generated code.

Empirical Testing Techniques for AI-Generated Systems

When you can't review the code, you must test the behavior. Here are the key techniques:

Performance and Scalability Testing

• Focus on how well the system meets its QARs
• Load testing, stress testing, and capacity planning become critical
• Monitor for performance degradation over time

Usability Testing

• Evaluate how effectively users can complete specific tasks
• Ensure the system can be used easily and productively
• Test with real users early and often

Change Cases

• Architectural change cases that directly impact QARs
• "Functional" change cases that indirectly impact QARs
• Test how the system responds to requirement changes

Security Testing

• Ethical hacking using the same tools as malicious attackers
• Chaos Monkey-style testing to identify vulnerabilities
• Automated security scanning and penetration testing

Resilience Testing

• Netflix's Chaos Monkey approach: randomly terminating instances to test system resilience
• Circuit breaker testing
• Failover and disaster recovery validation

The "Caveat Prompter" Principle

Using AI to generate the MVA doesn't change the basic fact about software architecture: teams still have to make decisions about trade-offs. The team needs to know what trade-offs it may need to make and articulate those in the prompts to the AI.

This means teams need to be explicit about trade-offs and alternatives when creating AI prompts. The AI then works as a very clever search engine to find possible solutions that might address the trade-offs.

We call this the "caveat prompter" principle: you have to understand the problem and the trade-offs, or you won't be able to provide enough information to the AI to generate a good result.

The Sustainability Question

Perhaps the most dangerous aspect of AI-generated architecture is the sustainability question. Teams may have delivered a system that works today but could break at some point, and they may not be able to fix it.

The quality of AI-generated code may degrade over time as newer models are trained on poorer quality code (often itself AI-generated). This creates a potential death spiral where each generation of AI-generated code is worse than the last.

Conclusion: Architecture Isn't Dead, It's Evolving

Does AI-generated code bring an end to software architecture? No. Teams still need to make architectural decisions and trade-offs, but they need to be far more articulate about their reasoning so they can supply the AI with these ideas in the prompts.

Like any technology, AI solves some problems but creates new ones. With a successful MVP and an MVA depending on AI-generated code, development teams may be playing a dangerous game.

To navigate this new landscape, teams must:

1. Shift from up-front design to empirical validation
2. Develop new testing capabilities focused on QARs
3. Be explicit about trade-offs in AI prompts
4. Consider long-term maintainability and supportability
5. Build automated testing infrastructure

The future of software architecture isn't about designing perfect systems up front—it's about building the capability to empirically validate and evolve systems that you can't fully understand.