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SKU/Artículo: AMZ-B0GNCV458X

VECTOR SEARCH FOR AGENTIC AI: DESIGNING SCALABLE MEMORY AND RETRIEVAL FOR INTELLIGENT AGENTS

Format:

Kindle

Kindle

Paperback

Detalles del producto

Disponibilidad:
Fuera de stock

Peso con empaque:
0.94 kg

Devolución:
No

Condición
Nuevo

Producto de:
Amazon

Viaja desde
USA

Sobre este producto

About the Technology: Artificial intelligence is evolving beyond static models and simple prompt-response systems. Today’s most powerful AI systems are becoming agentic — capable of reasoning, planning, remembering, adapting, and acting across complex environments. At the center of this transformation is vector search: the technology that enables machines to store meaning, retrieve context, and reason over knowledge instead of keywords. Vector embeddings, semantic similarity, memory indexing, retrieval-augmented generation, and scalable memory architectures are no longer experimental ideas — they are now the foundation of modern intelligent systems. Vector Search for Agentic AI explores this transformation at a deep technical level, showing how memory, retrieval, and semantic understanding are becoming the core infrastructure of autonomous intelligence. This is not about chatbots. This is about building systems that think, learn, adapt, and evolve through memory. Summary of the Book: This book is a complete technical and architectural guide to designing scalable memory systems for intelligent agents. It explains how vector databases, embeddings, semantic retrieval, and long-term memory models combine to form the cognitive backbone of agentic AI systems. You will learn how intelligent agents move beyond simple responses into persistent identity, adaptive behavior, contextual reasoning, and autonomous decision-making. The book connects theory with engineering, showing how to design memory pipelines, retrieval layers, reasoning-aware embeddings, and scalable vector infrastructures that support real-world production systems. Rather than abstract theory, this book focuses on practical implementation, real architectures, and production-grade design patterns used in advanced AI systems. It explains how memory becomes intelligence, how retrieval becomes reasoning, and how agents become systems rather than tools. This is not a surface-level introduction. It is a deep, structured blueprint for building true intelligent agents. What’s Inside: Inside this book, you will learn how vector embeddings represent meaning, how semantic similarity enables reasoning, and how memory structures evolve into cognitive architectures. You will explore the engineering of long-term memory systems, multimodal vector storage, reasoning-aware embeddings, persistent agent identity, and scalable retrieval pipelines. You will understand how vector search integrates with planning systems, decision engines, and autonomous workflows. The book walks through real design models for memory indexing, retrieval optimization, context compression, memory evaluation, drift detection, memory governance, and system safety. You will see how modern agent systems use memory as infrastructure, not storage, and how retrieval becomes the foundation of intelligence rather than a supporting feature. Every concept is connected to real system design, real architectures, and real implementation logic — not theory without application. About the Reader: This book is written for builders. For engineers designing intelligent systems. For developers building autonomous agents. For founders creating AI platforms. For researchers exploring memory-driven intelligence. For architects designing scalable AI infrastructure. For professionals who want to move beyond basic AI usage into AI system engineering. If you are interested in how AI systems truly work under the hood — how memory, retrieval, reasoning, and autonomy connect — this book was written for you.

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