AI-Augmented Domain-Driven Design: Design Better Domains with LLMs, AI Agents, and Multi-Agent Workflows (en Inglés)
Reseña del libro "AI-Augmented Domain-Driven Design: Design Better Domains with LLMs, AI Agents, and Multi-Agent Workflows (en Inglés)"
Learn how to apply AI to Domain-Driven Design using LLMs, prompt engineering, and multi-agent systems. Build repeatable domain modeling workflows that improve software architecture, clarity, and consistency. Key Features Use LLMs as structured reasoning partners for Domain-Driven Design Apply prompt engineering to domain discovery and modeling workflows Design AI agents and multi-agent systems for DDD workflows Improve software architecture decisions while reducing cognitive overload Build domain models with AI-assisted EventStorming techniques Book Description Modern software systems are increasingly complex. Distributed architecture, regulatory pressure, integration sprawl, & sociotechnical fragmentation place significant cognitive demands on architects & developers. Domain-Driven Design (DDD) remains one of the most effective ways to manage complexity, but it depends on sustained human reasoning & shared understanding. AI-Augmented DDD introduces a practical approach to using LLMs as structured reasoning partners rather than code generators. Instead of automating decisions, you'll learn to amplify human thinking, reduce cognitive overload, & preserve conceptual clarity across evolving systems. The book begins by building a clear mental model of how LLMs operate, helping you understand their strengths & limitations. You’ll then apply prompt engineering techniques to domain modeling using structured prompts, rulebooks, EventStorming patterns, & iterative refinement. Through case studies, you'll progressively construct events, commands, aggregates, & bounded contexts using AI-assisted reasoning. Finally, you'll design AI agents for domain discovery, storytelling, & context mapping, orchestrating them into scalable multi-agent systems for DDD. By the end of the book, you'll be able to integrate AI into your software architecture practice in a disciplined, repeatable, & future-ready way. What you will learn Understand how LLMs reason and where they fail Apply prompt engineering to domain modeling activities Use AI to refine domain events and bounded contexts Build rulebooks to guide and constrain AI reasoning Design AI agents for Domain-Driven Design workflows Build multi-agent systems for domain modeling workflows Reduce cognitive overload in complex software systems Preserve domain clarity across evolving software architectures Who this book is for This book is for software developers, software architects, and technical leaders working on complex business systems who want to strengthen their Domain-Driven Design practice using AI. Readers should be comfortable with software architecture concepts and familiar with DDD fundamentals such as bounded contexts, aggregates, domain events, and domain modeling. No prior AI expertise is required, but an interest in LLMs, prompt engineering, and AI-assisted reasoning will help you get the most from this practical guide. Table of Contents The AI Landscape: From Hype to Utility How LLMs Think (and Why It Matters) The Architect's View: Reasoning with AI Systems From Domain Exploration to AI Collaboration Defining Rules: Teaching the Model to Think in DDD Iterative Refinement: Building the Bounded Context Prompt as Knowledge Carrier From Prompts to Agents: Structuring AI Collaboration Designing Specialized Agents for DDD Tasks The Orchestrator: Building the Main Agent Governed Autonomy: Balancing Autonomy and Alignment Across Agents
