AGENTIC SPEC-DRIVEN DEVELOPMENT WITH AI AGENTS: A Practical Guide to Requirements, Specifications, Planning, Validation, and Reliable Software Development (en Inglés)
Reseña del libro "AGENTIC SPEC-DRIVEN DEVELOPMENT WITH AI AGENTS: A Practical Guide to Requirements, Specifications, Planning, Validation, and Reliable Software Development (en Inglés)"
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 requirements Write structured requirements with EARS, acceptance criteria, invariants, state transitions, and measurable quality attributes Separate required behavior from implementation decisions and document architecture with C4 views and ADRs Create executable contracts with OpenAPI, JSON Schema, AsyncAPI, compatibility rules, and contract testing Break specifications into bounded agent tasks with dependencies, stop conditions, completion evidence, migration plans, and rollback strategies Engineer agent context using project instructions, memory, reusable skills, tools, MCP servers, sandboxes, permissions, hooks, and checkpoints Design reliable single-agent and multi-agent workflows with context isolation, handoffs, parallel execution, and scope controls Build stronger verification with unit, integration, contract, end-to-end, property-based, fuzz, state-machine, and mutation testing Use protected tests, independent review, evals, and failure diagnosis to reduce self-confirming AI mistakes Trace goals and requirements through design, tasks, code, tests, release evidence, and production behavior Handle brownfield systems with bugfix specifications, delta specifications, compatibility analysis, and specification convergence Secure agentic development against prompt injection, memory poisoning, tool abuse, excessive privilege, and supply-chain risk Connect CI/CD, progressive delivery, rollback, observability, SLOs, and incident learning back to the specification process Throughout 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.
