Your Product Is Full of MVPs: Flexible Problem Solving with AI Agents

A product is never uniformly mature. Some features are proven and stable; others are still experiments. Every part of a product sits on two axes: how uncertain it is, and how costly a failure would be. Uncertainty tells us how freely to explore; the cost of failure tells us where we can't cut corners. AI agents make exploration almost free, but they don't know which zone they're working in unless we tell them. So we need to make those zones explicit, and use modular architecture to enforce the boundaries between them.

Your product is full of MVPs

The usual story is that you vibe code an MVP to test an idea, then throw it away and build the real product properly. But in a real product, MVPs don't disappear. They become part of the product, and they are often what moves it closer to product–market fit. Inside a living product, the MVP doesn't go away; it becomes a minimal viable feature (MVF).

Every product contains features at different stages of maturity. Some are well understood, battle-tested and relied upon. Others are new, with uncertain requirements and interactions. Treating them all with the same process is wasteful: over-engineering new ideas slows learning, while under-engineering critical features creates risk.

The goal isn't to make every feature equally mature. It's to let each feature mature at the right pace, without compromising the rest of the product.

Two questions, not one

It's tempting to think of maturity as a single scale, from experimental to battle-tested, with rigor increasing as you go. But two different questions are at play:

• How uncertain is it? How well do we understand the problem and the solution? High uncertainty calls for exploration: prototyping alternatives, shipping MVFs and learning from users.

• How costly is a failure? What happens if this goes wrong? A high cost of failure calls for rigor: explicit contracts, constrained changes, reviews and reliable tests.

These axes are independent. Consider an activity feed showing what colleagues are working on. What belongs in the feed, how updates should look, and when notifications are useful are all highly uncertain, and getting them wrong is cheap. You can experiment quickly, ship an MVF and learn from users. But an event from a private project appearing in someone else's feed is a data leak. That part of the feature is brand new, yet it is critical from day one. The experience can be immature; the access controls cannot.

Combining the two questions gives four ways of working:

• Uncertain and low-stakes: Explore freely. Let agents prototype alternatives, and expect to throw most of them away.

• Uncertain and high-stakes: Explore the experience, but route everything risky through established, rigorously engineered components.

• Understood and low-stakes: Improve incrementally. A light process is enough.

• Understood and high-stakes: Use explicit contracts, constrained changes, reviews and reliable tests.

The right approach therefore depends less on the project, or even the feature, than on the specific part you're touching. A mature feature can contain a new experimental workflow, a new feature can depend on well-established infrastructure, and a single feature can be wide open in its user experience while being non-negotiable in its security.