What if your AI could not just answer questions, but think, reason, plan, and act like an autonomous expert? In Applied AI Agent Engineering, Ronald Laffey takes you beyond traditional prompt engineering into the world of production-ready AI agents, where large language models (LLMs) work alongside Retrieval-Augmented Generation (RAG) and knowledge graphs to solve complex real-world problems.
This book is your definitive guide to building robust, scalable, and trustworthy AI agents for enterprise applications. You will learn how to design intelligent systems that can plan, execute, collaborate, and evolve, all while maintaining precision, reliability, and explainability. From constructing hybrid RAG pipelines to orchestrating multi-agent workflows, and from integrating tools and APIs to deploying agents at scale, this book equips you with the practical skills and architectural insights needed to transform your AI projects from prototypes to production-ready solutions.
Inside, you'll discover how to:
Combine LLMs with structured knowledge graphs to reduce hallucinations and enhance reasoning.
Design and implement GraphRAG architectures for complex, multi-step problem-solving.
Build multi-agent systems capable of collaboration, reflection, and decision-making.
Apply real-world production engineering practices, including evaluation metrics, monitoring, and MLOps.
Deploy local-first and enterprise-scale agents while ensuring security, governance, and responsible AI.
Unlike other AI guides, this book goes beyond theory, giving you hands-on frameworks, code examples, and practical workflows that you can apply immediately. It's designed for developers, engineers, and AI enthusiasts who want to move from experimentation to creating AI agents that truly perform.
If you're ready to take your AI skills to the next level, turn the page and start building the autonomous, intelligent systems that will define the future of enterprise AI.
