Why Semantic App Definitions Are Emerging as the Missing Layer for AI-Built Software

For the past two years, the dominant narrative around AI and software development has been simple: large language models can now write code. Frontend components, backend services, database schemas — all generated from prompts.

In practice, this approach has hit predictable limits. AI-generated code is often impressive in demos, but fragile in production. It can be hard to audit, difficult to evolve, and expensive to maintain. The deeper problem isn’t model capability — it’s that raw code is a poor interface between human intent and machine execution.

A growing number of platforms — and now major players — are converging on a different idea: AI should operate on semantic models, not free-form code.

Google’s recent introduction of A2UI, an open project for agent-driven interfaces, is a clear signal in this direction. Rather than having AI generate UI code directly, A2UI defines a declarative schema that describes what an interface contains and lets a trusted runtime render it appropriately. The AI produces structure and intent; the system handles execution.

That shift is small in scope — A2UI focuses narrowly on UI to be generated on the fly by AI — but significant in implication. It reflects a broader realization: semantic representations are a more stable boundary for AI than executable code.

The Core Problem with AI Code Generation

The appeal of AI-generated code is obvious: it feels direct, flexible, and expressive. But code is also:

• Highly contextual and brittle

• Closely coupled to specific frameworks and versions

• Difficult to verify for correctness or security

• Poor at preserving intent over time

When AI emits code, it collapses what the app is supposed to do and how it’s implemented into a single artifact. Any change — even a small one — risks breaking that implicit contract.

This is why many AI-generated applications struggle to move beyond prototypes. The output works once, but it doesn’t age well.

Semantic App Definitions: A Different Abstraction Boundary

A semantic app definition separates meaning from execution.

Instead of generating code, the system works with a structured, declarative representation of the application:

• What screens exist and how users navigate

• What data entities exist and how they relate

• What actions are allowed and under what conditions

• What roles and permissions apply

• How UI elements are composed from known patterns

Crucially, this representation is not executable. It’s interpreted by a runtime engine that handles rendering, state management, data access, and platform-specific behavior.

This is the same architectural move A2UI makes for interfaces — but extended across the entire application stack.

Why This Enables True No-Code (and “No-Code Off-Ramps”)

One underappreciated benefit of semantic definitions is how they enable no-code off-ramps.

Because the application is represented as a simplified, declarative structure:

• Non-developers can inspect and modify it

• Changes can be made visually or via structured editors

• AI can suggest updates without introducing opaque logic

• The underlying execution engine remains isolated and trusted

In other words, users aren’t editing code — they’re editing the meaning of the app. Execution is handled elsewhere.

This separation is what makes no-code sustainable. Without it, “no-code” tools inevitably accumulate hidden technical debt behind visual abstractions. With it, complexity stays contained.

Google’s A2UI as Industry Validation

Google’s A2UI project is intentionally modest. It doesn’t attempt to define full applications, data models, or workflows. Instead, it provides a declarative format that allows AI agents to describe UI layouts safely and consistently across platforms.

That narrow scope is precisely why it’s revealing.

Google isn’t trying to make AI write better UI code. It’s removing code from the loop entirely and replacing it with a semantic contract between AI and runtime.

This aligns closely with the broader semantic-first architectures emerging in application platforms like Buzzy, which apply the same principle to entire applications — not just interfaces.

A2UI is not a competitor to these systems. It’s a validation of the underlying idea.

Tradeoffs, Clearly Stated

Semantic systems are not without constraints.

They trade some expressive freedom for:

• Predictability

• Maintainability

• Auditability

• Platform independence

Free-form code generation can feel more powerful in the short term. Semantic definitions are more disciplined — but they scale better over time.

Given today’s reality — where AI-generated code is often untrusted, difficult to evolve, and expensive to maintain — that tradeoff increasingly favors semantic approaches.

Where This Leaves AI-Driven Development

The trajectory is becoming clearer:

• AI will increasingly generate structured intent, not executable artifacts

• Trusted engines will interpret that intent across platforms

• Application definitions will become durable, versionable assets

• Humans and AI will collaborate at the semantic level

  
  

Google’s A2UI is an early public signal of this shift. Systems like Buzzy demonstrate what it looks like when the same idea is applied end-to-end.

The future of AI-built software isn’t about writing more code faster. It’s about choosing the right abstraction boundary — and semantic app definitions may be the one that finally holds.

About the Author

Adam Ginsburg is the founder of Buzzy, a platform redefining how applications are built with AI. With deep experience in no-code development, AI-augmented workflows, and product strategy, Adam focuses on bridging the gap between human intent and machine execution. He writes about the evolving landscape of AI in software development, emphasizing semantic models, maintainability, and sustainable innovation. Connect with him to explore the future of AI-native application creation.