AI-Native Engineering

AI-native engineering does not mean "uses an autocomplete". It means the whole loop is built around agents: specification, scaffolding, implementation, testing, review, refactoring and release. In that loop, the scarce skill moves from typing code to specifying intent precisely, verifying output rigorously, and deciding what must stay under human control. The same skills apply when the model is inside the product, where the questions become grounding, cost, evaluation and what the model is never allowed to do.

Twelve parts. What AI-native means and the loop it changes; specs as the agent's working memory (the three-layer model, personas and skills); agents in the loop, from one assistant to orchestrated teams, and when to stop them; what agents test well and badly, and review as teaching; codebases legible to engineers and agents alike; guardrails, including the line around money flows, plus cost and eval harnesses; and AI products, with grounding, cost firewalls, deterministic stubs and evaluation as a release gate. Five deeper parts follow: context engineering and retrieval, evals in depth, agent architecture with tools, state and interrupts, security for AI features, and the frontend of AI products.