Definition. AI-native software is designed around a combination of deterministic application logic and probabilistic intelligence, with the contracts and safeguards needed to make that combination dependable.
Do not replace deterministic code unnecessarily
Authentication, billing, invariants and critical state transitions should remain explicit wherever possible. AI belongs where it reduces ambiguity, accelerates reasoning or creates an interface that deterministic code alone would struggle to provide.
Move intelligence into a controlled architecture
Model selection, context, retrieval, tools, memory, evaluation and policy belong behind explicit application boundaries. This makes individual providers replaceable and keeps the product logic understandable.
Design for disagreement
Probabilistic outputs can be wrong. Give the system ways to abstain, ask a human, use a deterministic fallback, cite source material or retry safely depending on the task.
Measure the product outcome
Evaluate successful task completion, time saved, error reduction, quality and operational cost. A higher model score is not the same thing as a better product if users still cannot complete the task reliably.
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