ai-first-engineering

An AI-first engineering workflow is an operating model for teams where AI agents generate implementation output. You need governance to shift review focus from syntax to system behavior, ensuring reliability through explicit contracts and deterministic tests.

To set up code review for AI-generated code, shift focus from syntax to system behavior. Evaluate architecture boundaries, typed interfaces, and deterministic tests to ensure the AI-driven delivery meets measurable acceptance criteria and explicit contracts.

Define measurable acceptance criteria for AI-driven delivery by emphasizing decomposing ambiguity and enforcing risk controls. Require regression coverage for touched domains, explicit edge-case assertions, and integration checks for interface boundaries.

Testing standards for AI-generated implementation require regression coverage for touched domains, explicit edge-case assertions, and integration checks for interface boundaries. Eval coverage matters more than anecdotal confidence to ensure system reliability.

You can apply this AI-first operating model to product teams. It is designed for software engineering and product teams that rely on AI agents, requiring architecture boundaries, stable contracts, typed interfaces, and deterministic tests.

Planning quality matters more than typing speed in AI-driven engineering because implementation output is generated by AI agents. The operating model requires defining measurable acceptance criteria, enforcing risk controls, and maintaining explicit architecture boundaries to ensure reliable delivery.