Spec-driven Development: How AI Changed Everything (And Nothing) by Simon Martinelli @ Spring I/O 26

Specification-driven development prioritizes detailed requirements as the primary contract between business needs and AI-generated code. By focusing on domain models and system use cases, developers act as enablers who guide AI agents to produce consistent, testable software. This approach is particularly effective in Brownfield modernization projects, where existing codebases are reverse-engineered into structured specifications to facilitate feature expansion and system upgrades. Success relies on maintaining human oversight, as AI remains non-deterministic and requires expert verification of the generated output. Integrating tools like Model Context Protocol (MCP) servers and custom agent skills allows for repeatable, stack-specific development workflows. Ultimately, this methodology reduces reliance on manual coding, improves project sustainability, and ensures that the resulting architecture remains aligned with business intent rather than just the idiosyncrasies of the AI model.