Practical Spec-Driven Development with AI Agents

Practical Spec-Driven Development with AI Agents: Build Reliable Software Through Requirements Engineering, Agentic Coding, Automated Testing, and Continuous Delivery

Stop relying on AI-generated code that works today but breaks tomorrow. Build software you can confidently test, maintain, and deploy.

AI coding agents accelerate development, but vague requirements, inconsistent outputs, missing tests, and security vulnerabilities can undermine software quality.

For developers and engineering teams, writing code faster means little if the result is difficult to verify or maintain.

What if you could turn requirements into clear specifications that guide AI agents toward reliable, testable software?

Practical Spec-Driven Development with AI Agents teaches you how to apply specification-driven development to real software projects, combining requirements engineering, controlled AI coding, automated testing, and continuous delivery.

Through practical examples and step-by-step workflows, you'll learn to:

• Write actionable specifications using acceptance criteria, architecture decisions, and requirements traceability.

• Control AI coding agents with GitHub Spec Kit, OpenSpec, structured tasks, and systematic code reviews.

• Build reliable applications using TypeScript, Node.js, Fastify, and PostgreSQL.

• Verify generated code through automated tests, API contract validation, security checks, and regression testing.

• Streamline software delivery with Docker, GitHub Actions, CI/CD pipelines, and deployment verification.

Designed for software developers, AI-assisted programmers, and technical leads, this book connects modern AI coding workflows with practical software engineering principles.

Learn by building a real application, not just reading about development frameworks.

Across 12 chapters and 59 technical diagrams, you'll follow the development of a team-based issue-tracking API, from initial requirements through architecture, implementation, testing, security, and deployment preparation.

The book includes working-oriented code examples, reusable specification templates, engineering checklists, testing scenarios, and troubleshooting techniques.

You'll examine practical challenges such as unauthorized access, concurrent database updates, specification drift, and unreliable agent-generated changes.

By applying these methods, you can develop a more structured approach to AI-assisted programming, reduce avoidable ambiguity, and make software changes easier to review and verify.

Take control of your AI coding workflow. Get your copy today and start building software with clearer requirements, stronger verification, and more reliable delivery practices.