ai-first-engineering

Shift review focus from syntax and style to system behavior: check behavior regressions, security assumptions, data integrity, failure handling, and rollout safety. Automate style checks so reviewers spend time only on high-signal concerns.

Agent-friendly architectures use explicit boundaries, stable contracts, typed interfaces, and deterministic tests. Avoid implicit behavior spread across hidden conventions, which agents cannot reliably infer or maintain.

Require regression coverage for touched domains, explicit edge-case assertions, and integration checks for interface boundaries. The testing bar should be higher than for hand-written code because generated code lacks author intuition.

It fits poorly when AI agents contribute little implementation output, when domains demand deep manual verification (e.g., safety-critical systems), or when teams lack the eval and testing infrastructure the model depends on.

Look for engineers who decompose ambiguous work cleanly, define measurable acceptance criteria, produce high-signal prompts and evals, and enforce risk controls under delivery pressure rather than those optimizing for raw coding speed.