Spec Driven Development: Specs AI Agents Can Build From

Spec-driven development treats a written specification of intended behavior as the primary source of truth, with AI agents generating code from it, shifting the main challenge from implementation to defining precise requirements and verifying compliance. Unlike earlier approaches such as behavior-driven or model-driven development, AI can directly consume specifications, reducing documentation drift, while tools such as GitHub Spec Kit organize work into principles, requirements, technical plans, tasks, implementation, and convergence checks. Buildable specifications use individually testable, stable acceptance criteria, explicit constraints, and non-goals rather than vague qualities like speed or intuitiveness. Verification remains essential because generated code can appear clean and functional while misunderstanding requirements; teams should link tests to acceptance-criterion IDs, fail CI for untested criteria, and detect orphaned tests tied to removed requirements. At larger scale, traceability systems can connect requirements, tests, execution results, and defects across teams and releases. The method is less suitable for prototypes, research, and other situations where coding helps discover unknown requirements, and it can fail when teams edit generated code without updating the specification or treat code generation as proof of completion.

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