ai-first-engineering

Code review for AI-generated code should shift focus from syntax to system behavior, prioritizing behavior regressions, security assumptions, data integrity, failure handling, and rollout safety. Automation should handle style issues to minimize manual review time.

An AI-first engineering operating model defines workflows where AI agents generate a large share of implementation output. It shifts focus from typing speed to planning quality, eval coverage, and rigorous review gates to ensure reliable delivery.

Agent-friendly architectures require explicit boundaries, stable contracts, typed interfaces, and deterministic tests. They avoid implicit behavior spread across hidden conventions to ensure AI agents can reliably generate and integrate code.

Testing standards for AI-generated code require raising the testing bar, including required regression coverage for touched domains, explicit edge-case assertions, and integration checks for interface boundaries to ensure deterministic behavior.

Strong AI-first engineers decompose ambiguous work cleanly, define measurable acceptance criteria, produce high-signal prompts and evals, and enforce risk controls under delivery pressure. These signals matter more than raw coding speed.

AI-first engineering requires explicit boundaries, typed interfaces, and governance workflows to ensure reliable, safe delivery. Without these architectural controls, AI agents risk introducing implicit behavior and system regressions.