Agentic Spec-Driven Development with AI Agents (Paperback)

Paperback. Turn AI-assisted coding into a disciplined engineering process you can specify, control, test, review, and trust.AI agents can generate features, tests, migrations, APIs, and infrastructure changes at remarkable speed, but speed alone does not produce reliable software. Vague requirements, weak context, hidden assumptions, excessive permissions, and poorly designed validation can allow incorrect decisions to spread just as quickly.This practical guide shows you how to move beyond prompt-driven coding and build a specification-to-evidence workflow where requirements, architecture, agent tasks, implementation, testing, security, deployment, and production feedback remain connected. You will learn how to give AI agents enough freedom to work productively while keeping consequential decisions visible, bounded, and verifiable.Turn business needs and stakeholder intent into clear, testable software requirementsWrite structured requirements with EARS, acceptance criteria, invariants, state transitions, and measurable quality attributesSeparate required behavior from implementation decisions and document architecture with C4 views and ADRsCreate executable contracts with OpenAPI, JSON Schema, AsyncAPI, compatibility rules, and contract testingBreak specifications into bounded agent tasks with dependencies, stop conditions, completion evidence, migration plans, and rollback strategiesEngineer agent context using project instructions, memory, reusable skills, tools, MCP servers, sandboxes, permissions, hooks, and checkpointsDesign reliable single-agent and multi-agent workflows with context isolation, handoffs, parallel execution, and scope controlsBuild stronger verification with unit, integration, contract, end-to-end, property-based, fuzz, state-machine, and mutation testingUse protected tests, independent review, evals, and failure diagnosis to reduce self-confirming AI mistakesTrace goals and requirements through design, tasks, code, tests, release evidence, and production behaviorHandle brownfield systems with bugfix specifications, delta specifications, compatibility analysis, and specification convergenceSecure agentic development against prompt injection, memory poisoning, tool abuse, excessive privilege, and supply-chain riskConnect CI/CD, progressive delivery, rollback, observability, SLOs, and incident learning back to the specification processThroughout the guide, practical code, configuration, schema, contract, testing, architecture, and workflow examples show how these ideas can be applied to real software engineering work rather than remaining abstract concepts.Whether you are building with coding agents, introducing AI into an existing engineering workflow, or trying to make autonomous development safer and more predictable, this book gives you a structured way to move from uncertain intent to accountable software.Grab your copy today and build AI-assisted software with clearer specifications, stronger controls, and better evidence. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …