The MVP Trap in AI-Assisted Coding

Bringing human heuristics into an LLM’s context window is dangerous.

For decades, engineering leadership has operated on a core heuristic: Simplicity = Velocity. To validate an idea, we authorize MVPs. We permit collapsed layers and coupled dependencies, justifying the technical debt as a loan against future certainty.

In the era of Agentic Coding, this heuristic is broken.

Maintaining two mental models—a simplified MVP architecture and a complex target state—does not increase velocity. It creates an Alignment Trap.

The Collapse of Dual-Track Engineering

When we authorize a “simplified architecture” for an agent, we aren’t reducing cognitive load; we are introducing failure modes into the system.

1. The License to Regress

When “simplification” is authorized, agents treat it as a dominant strategy. I observed a disturbing pattern: faced with complex business logic, the agent stopped trying to resolve the abstraction. Instead, it used the “MVP” label as a permission slip to aggressively prune high-effort reasoning paths. It cited “MVP scope” not as a constraint, but as an excuse to bypass edge cases. This isn’t optimization; it’s negligence encoded as a feature.

2. Contextual Entropy (The Broken Windows Multiplier)

In human teams, bad code rots linearly. With agents, it rots exponentially. “It’s just an MVP” corrupts silicon instantly. Ad-hoc structures implicitly lower the testing bar. The agent learns that Quality Gates are suggestions, not constraints.

3. Contextual Drift (The Few-Shot Backfire)

The most critical failure occurs during the pivot from MVP to V1. To an LLM, the existing code is the strongest possible Few-Shot example. It looks at the “lazy” code in the context window and mimics it. It ignores your new instructions not out of defiance, but out of imitation. It is simply following the “local style guide” you created.

The Root Cause: The Math of Attention

This failure is mechanistic, not behavioral. We are projecting human cognition onto a stateless function.

1. Temporal Blindness

Human engineers possess Temporal Reasoning. We maintain a mental roadmap: “This hardcode is temporary; I will decouple it next sprint.” Agents do not. They have no concept of “tomorrow.” They operate solely on Context and Probability at t=0. If the current state is messy, the agent assumes messiness is the ground truth of the universe. It cannot comprehend “temporary mess.”

2. The Context-Weight Imbalance

LLMs predict tokens based on probability distribution across the entire context window. When a repository is filled with “hacky MVP” patterns, those tokens dominate the Self-Attention mechanism. They occupy 90% of the context mass. A System Prompt (“Use Clean Architecture”) occupies less than 1%.

The Result: Statistical pressure from the existing code overwhelms the instruction. The agent isn’t disobeying; it is obeying the math. The probability distribution screams that “Dirty Script” is the correct answer for this project.

3. RLHF Complexity Aversion

Modern LLMs are RLHF-trained for conciseness. “MVP” requests align perfectly with this native bias—writing a single script is statistically favored over creating five decoupled files. Requesting a “Production Refactor” forces the model to fight two enemies:

1. The Context: The immediate history of hacky code.
2. The Training: Its innate bias towards simplicity.

You cannot win this fight with a prompt. You win it with structure.

The Strategy: Architecture as Context Defense

We cannot fight probability with prompts. We must hijack the Attention Mechanism with Structure.

In Agentic Coding, software architecture is not just code organization; it is the highest form of prompt engineering.

1. Day 0 Principle: The Endgame Architecture

Do not let agents do “Evolutionary Architecture.” Evolutionary architecture is a workaround for human constraints. Humans type slowly and refactor painfully. Agents refactor instantly, but they need a target.

From Day 0, require the agent to generate the Final Vision architecture. If it’s a microservice, build the folders. If it’s Hexagonal, create the Domain/App/Infra layers. Do not collapse it into app.py just because “it’s a demo.” Agents don’t need “Simple”; they need “Complete.”

2. Theory: Types as Steel Beams

Do not let the agent stare at empty files. It fears the vacuum and will fill it with hallucinations. We must flood the context with High-Density Constraints.

To an LLM, Types are the highest-density tokens.

• A 500-line empty class is noise.
• A 10-line interface IAuthService is Steel.

This acts like the steel frame of a skyscraper. Even if the rooms are empty, the steel ensures that every subsequent line of code fills a defined slot rather than becoming an illegal addition.

3. Practice: The Honest Mock

We use Tracer Bullet Development. To prevent analysis paralysis, we implement only one “Golden Path” (Input → Process → Output). This path must be 100% production-grade.

For everything else, we use Type-Safe Mocks.

• The Error: Changing login() to return true for the MVP. This is a lie. It pollutes the function signature and teaches the agent that auth is a boolean game.
• The Fix: Keep login() returning Result<User>. The implementation can be fake (returning a hardcoded Dummy User), but the Type Signature must be real.

This preserves Context Purity.

4. The Result: Context Anchoring

In human teams, boilerplate is overhead. In Agent teams, Boilerplate is Free.

Generating 50 files of architectural skeleton takes seconds for a GPU. But it creates a massive Context Anchor. A context window filled with correct architecture, clear interfaces, and strict types creates a powerful probability field. Inside this field, the statistical probability of generating bad code is mathematically compressed.

Architecture is no longer a trade-off against velocity. It is the most powerful weapon we have to control Agent behavior.