{"product_id":"designing-agentic-search-architectures-a-systems-engineering-guide-to-building-self-correcting-low-latency-retrieval-pipelines-for-complex-data-silo-9798197778048","title":"Designing Agentic Search Architectures: A Systems Engineering Guide to Building Self-Correcting, Low-Latency Retrieval Pipelines for Complex Data Silo","description":"\u003cp\u003e • Author(s): Ethan Tyson\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Programming - Algorithms\u003c\/p\u003e\u003cp\u003e\u003cb\u003eDesigning Agentic Search Architectures: A Systems Engineering Guide to Building Self-Correcting, Low-Latency Retrieval Pipelines for Complex Data Silos\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eEnterprise search breaks when real data gets messy, scattered, outdated, permissioned, and too complex for a single vector lookup to handle.\u003c\/p\u003e\u003cp\u003eAre your RAG systems missing critical context, returning weak answers, or slowing down when queries span documents, databases, APIs, and knowledge graphs? Do you need retrieval pipelines that can plan, evaluate, retry, route, and correct themselves before the final answer reaches the user?\u003c\/p\u003e\u003cp\u003e\u003cb\u003eDesigning Agentic Search Architectures\u003c\/b\u003e gives engineers and AI architects a practical systems guide for building modern retrieval pipelines that go beyond passive search. This book shows how to design agentic search systems that combine state machines, query expansion, multi-agent routing, corrective RAG, heterogeneous storage, MCP-based ingestion, caching, benchmarking, and security controls into production-ready retrieval workflows.\u003c\/p\u003e\u003cp\u003eInside, you'll learn how to build search architectures that can: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eDecompose complex questions into executable retrieval plans\u003c\/li\u003e\n\u003cli\u003eRoute tasks across vector databases, SQL systems, graph databases, and external APIs\u003c\/li\u003e\n\u003cli\u003eUse corrective RAG loops to evaluate, prune, retry, and verify retrieved context\u003c\/li\u003e\n\u003cli\u003eReduce latency and token cost with caching, streaming, and prompt compression\u003c\/li\u003e\n\u003cli\u003eSecure enterprise retrieval with RBAC, auditing, tenant isolation, and controlled tool access\u003c\/li\u003e\n\u003cli\u003eTest and benchmark agentic retrieval pipelines before deployment\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eWhat makes this book different is its systems-engineering focus. Instead of treating AI search as a simple prompt-and-vector problem, it shows how to structure retrieval as a controlled, observable, self-correcting architecture built for complex data silos.\u003c\/p\u003e\u003cp\u003eThis book is for AI engineers, software engineers, data engineers, solution architects, and technical leaders building serious RAG, agentic AI, enterprise search, and retrieval infrastructure.\u003c\/p\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":47891230294167,"sku":"9798197778048","price":2199.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798197778048.webp?v=1781182773","url":"https:\/\/atlanticbooks.com\/products\/designing-agentic-search-architectures-a-systems-engineering-guide-to-building-self-correcting-low-latency-retrieval-pipelines-for-complex-data-silo-9798197778048","provider":"Atlantic Books","version":"1.0","type":"link"}