Principles
Core principles for building MVPs with AI agents without losing learning discipline.
Key takeaways
- Agentic work moves the bottleneck: as implementation speeds up, unclear intent, weak evidence, and poor quality gates cost more.
- Follow five principles, including evidence before elegance, one hypothesis per build, and prewritten decisions.
- With agents, review shifts from line-by-line authorship to outcome, risk, and regression control, and specs must be sharper.
- Avoid anti-patterns like building a full product just because the agent can, or treating vibe-coded output as validated.
Agentic MVP work changes the bottleneck. Implementation gets faster, so unclear intent, weak evidence, and poor quality gates become more expensive.
Principles
| Principle | Meaning |
|---|---|
| Evidence before elegance | A beautiful product is less useful than a clear market signal |
| One hypothesis per build | A sprint should answer one primary question |
| Context is the interface | Agents perform better when product, design, data, and constraints are packaged |
| Quality is scoped, not skipped | The MVP can be small, but the tested path must be trustworthy |
| Decisions are prewritten | Decide what evidence will cause iterate, pivot, pause, or scale |
What Changes With Agents
- More implementation paths can be explored in parallel.
- Specs must be sharper because agents execute ambiguity quickly.
- Review shifts from line-by-line authorship to outcome, risk, and regression control.
- The team can test more ideas, but only if analytics and decision rules keep up.
Anti-Patterns
- Building a full product because the agent can generate it.
- Running multiple hypotheses in one prototype.
- Treating vibe-coded output as validated product.
- Skipping instrumentation until after launch.
- Letting AI decisions affect users without review.
Fast Is Not Validated
Agentic speed makes false confidence easier. Keep the MVP small enough that every feature maps to a learning question.