{"product_id":"context-engineering-systems-timothy-m-noren-9798194127184","title":"Context Engineering Systems: A Practical Architecture for Building Reliable, Adaptive, and Context-Aware AI Applications","description":"\u003cp\u003eIt was 2:00 AM on a Tuesday, and the Slack notifications wouldn't stop.\u003c\/p\u003e\u003cp\u003eA tech lead I'll call Alex had just launched his company's new AI Assistant, built to answer employee questions across thousands of documents. The demo was a success. Executives applauded as it summarized policies in seconds.\u003c\/p\u003e\u003cp\u003eThen production began.\u003c\/p\u003e\u003cp\u003eWithin hours, an engineer bypassed access controls through prompt injection and exposed confidential salary information. Soon after, the AI began serving outdated 2022 expense policies because its vector database had fallen out of sync with SharePoint. Meanwhile, API costs were climbing and query latency had reached 12 seconds.\u003c\/p\u003e\u003cp\u003eAlex's team was operating a black box wrapped in hope.\u003c\/p\u003e\u003cp\u003eThe problem wasn't the LLM. It was the architecture around it.\u003c\/p\u003e\u003cp\u003eAfter restructuring the system with rigorous Context Engineering-including hard ACL pre-filtering, Change Data Capture (CDC) pipelines, and runtime guardrails-the chaos disappeared. Latency dropped by 60%, security vulnerabilities were addressed deterministically, and the AI became a trusted enterprise asset.\u003c\/p\u003e\u003cp\u003eThis book was written so you don't have to experience Alex's 2:00 AM panic call.\u003c\/p\u003eWhat You'll Learn\u003cp\u003eThis comprehensive guide covers the complete lifecycle of production-grade enterprise AI systems: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eEvaluate AI System Quality: \u003c\/b\u003e Build automated LLM-as-a-Judge frameworks with Ragas and TruLens to measure faithfulness, context precision, and recall.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eImplement Hard AI Guardrails: \u003c\/b\u003e Defend against direct and indirect prompt injection, mask PII with Microsoft Presidio, and enforce strict JSON schemas through constrained decoding.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eManage Data Drift and Staleness: \u003c\/b\u003e Build real-time CDC pipelines with Kafka and webhooks to keep vector databases synchronized with enterprise data sources.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eBuild AI Observability: \u003c\/b\u003e Use OpenTelemetry to trace queries, monitor tokens and latency, detect retrieval degradation, and create real-time alerts.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eDesign Production Architectures: \u003c\/b\u003e Explore blueprints for enterprise knowledge assistants with RBAC\/ABAC, personalized customer-support agents with CRM integration and human escalation, and autonomous context-driven agents using ReAct planning, APIs, and self-healing workflows.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eUse Production-Ready Code: \u003c\/b\u003e Access Python implementations for hybrid search with Reciprocal Rank Fusion (RRF), recursive token chunking, and evaluation prompts for CI\/CD pipelines.\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003eWho This Book Is For\u003cp\u003eThis book is designed for professionals moving from AI prototypes to production-grade systems: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eAI and Software Engineers\u003c\/b\u003e building secure RAG pipelines and reliable agentic workflows.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eSystem and Cloud Architects\u003c\/b\u003e designing scalable, compliant, and cost-efficient AI infrastructure.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eTechnical Product Managers and Engineering Leaders\u003c\/b\u003e evaluating AI trade-offs, security risks, and operational requirements.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eData Engineers\u003c\/b\u003e responsible for vector indexing, metadata filtering, and real-time data synchronization.\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cb\u003ePrerequisites: \u003c\/b\u003e Basic Python knowledge and familiarity with REST APIs are enough to get started.\u003c\/p\u003e\u003cp\u003eThe era of toy AI demos and fragile wrapper scripts is over. Enterprise users expect AI systems that are fast, secure, accurate, observable, and resilient.\u003c\/p\u003e\u003cp\u003eYou can keep patching prompts and hoping your model behaves during the next client presentation-or learn the engineering principles required to build AI systems that work reliably in production.\u003c\/p\u003e\u003cp\u003eTake control of your context, enforce your security boundaries, and build enterprise intelligence that scales.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eGrab your copy of \u003ci\u003eContext Engineering\u003c\/i\u003e and start building production-ready AI with confidence.\u003c\/b\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Timothy M. Noren\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9798194127184\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Independently Published\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 08\/21\/2026\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 240\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 0.86lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.61h x 6.69w x 0.51d","brand":"Timothy M. Noren","offers":[{"title":"Paperback","offer_id":50847540216063,"sku":"9798194127184","price":35.0,"currency_code":"USD","in_stock":true}],"url":"https:\/\/www.whiterainbookhouse.com\/products\/context-engineering-systems-timothy-m-noren-9798194127184","provider":"WR Book House","version":"1.0","type":"link"}