Spec-Driven Development (ainativedev.io)

Spec-driven development reframes how teams work with AI agents: instead of hand-holding implementations, you write living, executable specifications that describe intent and let agents decompose and implement features. Paul Sawers distills ten core insights showing specs are structured documents (not one-off prompts) that align humans and agents, enable delegation, and act as reusable knowledge. He identifies four spec types—functional/technical (prompt.md/spec.md), agent identities (Agent.md), task/workflow specs, and usage/knowledge specs—and notes that mature AI-native stacks orchestrate multiple types together. The shift matters because it moves teams from ad‑hoc prompting to repeatable engineering practices, but it also surfaces new challenges around model sensitivity, tool fragmentation, and non‑deterministic outputs. Technically, specs are format‑agnostic (Markdown is common), yet they’re sensitive to model choice (Claude vs GPT‑4), context-window limits, and organizational info architecture. Practical solutions include RAG/context selection (even grep-based approaches for structured text), specialized subagents with focused context, AST parsing and LSP integration to bind specs to code, and backlog-driven planning to avoid “spec bloat.” Tests belong in specs; review remains essential, and separation of concerns (distinct agents for implementation vs testing) improves reliability. The ecosystem is early and fragmented—pick one tool, version specs (Git), iterate from a single feature, and plan for future spec registries and spec‑evaluation metrics to measure completeness, testability and maintainability.