FIELD NOTES FOR AI-NATIVE PRODUCT TEAMS
Shipping got faster. Product judgment did not.
Practical frameworks for the problems that appear after AI accelerates building: prototype debt, UI drift, context failure, wrong autonomy and product states nobody designed.
AI Product Readiness Scorecard.
15 checks across flow, states, system consistency, AI trust and recovery.
Why vibe-coded products feel unfinished even when they work.
The Prototype Debt Map: five layers between functional demo and coherent product.
How to stop AI coding agents from creating UI drift.
Five controls that stop inconsistency becoming the new standard.
What an agent-ready design system actually needs.
The operating layer between design intent, production components and coding agents.
AI agent UX: approval, autonomy, undo and recovery.
A practical model for deciding what an agent can do and where humans intervene.
From AI prototype to production-ready product.
A release-readiness sequence for teams that already have working code.
Design debt in AI-built products: fix recurrence, not screens.
Why repeated local decisions create more cost than visible inconsistency.
A better prompt cannot repair a broken context model.
Structure instructions, knowledge, memory, tools, permissions and task state.
The happy path is a demo. The state model is the product.
Loading, uncertainty, partial completion, failure and recovery before launch.