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AI Agent Memory & Context Engineering: A Practical Guide to Building Smarter, Personalized, and Scalable AI Agents with LangChain, RAG, and Vector Databases
AI Agent Memory & Context A Practical Guide to Building Smarter, Personalized, and Scalable AI Agents with LangChain, RAG, and Vector Databases
Discover how to give AI agents the power of memory and context unlocking smarter, more adaptive, and truly personalized systems.
Artificial intelligence has made huge leaps in language understanding, but without memory and context, most agents remain shallow and repetitive. This book shows you how to bridge that gap by engineering AI agents that remember, adapt, and scale with real-world applications.
From designing context-aware interactions to implementing retrieval-augmented generation (RAG) and vector databases, you’ll learn the technical and design strategies needed to build systems that go beyond one-off responses. With hands-on examples, case studies, and best practices, this guide equips practitioners and product builders to move from theory to production-ready solutions.
Whether you’re developing customer support bots, healthcare assistants, or fintech agents, this book provides the roadmap for aligning memory design with business goals while ensuring privacy, reliability, and compliance.
Practical guidance on integrating LangChain, vector databases, and RAG into AI workflows.
Case studies across industries including customer service, healthcare, and fintech.
Techniques for balancing accuracy, efficiency, and personalization in memory-powered agents.
Insights on ethical, regulatory, and transparency challenges in memory design.
Future-focused exploration of trends in decentralized and collaborative memory models.
If you’re ready to build the next generation of intelligent, memory-powered AI agents, get your copy today and start transforming your ideas into reality.
Discover how to give AI agents the power of memory and context unlocking smarter, more adaptive, and truly personalized systems.
Artificial intelligence has made huge leaps in language understanding, but without memory and context, most agents remain shallow and repetitive. This book shows you how to bridge that gap by engineering AI agents that remember, adapt, and scale with real-world applications.
From designing context-aware interactions to implementing retrieval-augmented generation (RAG) and vector databases, you’ll learn the technical and design strategies needed to build systems that go beyond one-off responses. With hands-on examples, case studies, and best practices, this guide equips practitioners and product builders to move from theory to production-ready solutions.
Whether you’re developing customer support bots, healthcare assistants, or fintech agents, this book provides the roadmap for aligning memory design with business goals while ensuring privacy, reliability, and compliance.
Practical guidance on integrating LangChain, vector databases, and RAG into AI workflows.
Case studies across industries including customer service, healthcare, and fintech.
Techniques for balancing accuracy, efficiency, and personalization in memory-powered agents.
Insights on ethical, regulatory, and transparency challenges in memory design.
Future-focused exploration of trends in decentralized and collaborative memory models.
If you’re ready to build the next generation of intelligent, memory-powered AI agents, get your copy today and start transforming your ideas into reality.
125 pages, Kindle Edition
Published September 7, 2025
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