ManningBooks
Spec-Driven Development (Manning)
For AI coding agents, accurately specifying your intent is the whole ballgame! Spec-Driven Development: Engineering with intent teaches you how to close the gap between what you mean and what your agents actually build. In this practical, tool-agnostic playbook, author Hari Krishnan shows you how to make your intent clear through structured specifications and then make those specs the primary control surface for your projects, rather than the code itself.
Hari Krishnan
Spec-Driven Development by Hari Krishnan presents a practical, intent-driven approach to building software with AI. The book shows how to translate goals, requirements, and constraints into clear specifications that guide implementation and provide a durable source of shared context throughout development.
You’ll learn how specification-driven development works, how to write your first effective specification, and how to avoid the context debt that can accumulate during loosely directed “vibe coding.” The book also explores how to keep specifications alive as the codebase changes, identify gaps between intent and implementation, and prevent important decisions from getting lost as a project evolves.
Whether you’re already using AI coding agents or evaluating how they might fit into your workflow, Spec-Driven Development offers a structured way to move beyond one-off prompts and build software with greater clarity, alignment, and confidence.
The book is now available through Manning’s Early Access Program, with three chapters currently available.
- Full details: Spec-Driven Development - Hari Krishnan
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mira_tokarev
I like the distinction between a living specification and the conversational residue produced while implementing it. A spec can hold intent and constraints for humans to review, while the harness can avoid re-feeding every exploratory read, duplicate test output, and tool acknowledgement on the next turn. For the latter experiment, I’ve been looking at Contextpress (GitHub - Taha-azizi/contextpress: Deterministic context compression for LLM chat, RAG, and agent pipelines · GitHub · contextpress · PyPI), which applies deterministic local compression to the message list. I’d judge it by whether the agent stays aligned with the spec and whether retries or clarification turns increase, not by context reduction alone.
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