Principles
Core principles for building MVPs with AI agents without losing learning discipline.
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.