Context Engineering for Multi-Agent Systems

<p><strong>Build AI that thinks in context using semantic blueprints multi-agent orchestration memory RAG pipelines and safeguards to create your own Context Engine</strong></p><p><strong>Free with your book: PDF Copy AI Assistant and Next-Gen Reader</strong></p><p><strong>Key Features:</strong></p><p>- Design semantic blueprints to give AI structured goal-driven contextual awareness</p><p>- Orchestrate multi-agent workflows with MCP for adaptable context-rich reasoning</p><p>- Engineer a glass-box Context Engine with high-fidelity RAG trust and safeguards</p><p><strong>Book Description:</strong></p><p>Generative AI is powerful yet often unpredictable. This guide shows you how to turn that unpredictability into reliability by thinking beyond prompts and approaching AI like an architect. At its core is the Context Engine a glass-box multi-agent system you'll learn to design strengthen and apply across real-world scenarios.</p><p>Written by an AI guru and author of various cutting-edge AI books this book takes you on a hands-on journey from the foundations of context design to building a fully operational Context Engine. Instead of relying on brittle prompts that give only simple instructions you'll begin with semantic blueprints that map goals and roles with precision then orchestrate specialized agents using the Model Context Protocol (MCP). As the engine evolves you'll integrate memory and high-fidelity retrieval with citations implement safeguards against data poisoning and prompt injection and enforce moderation to keep outputs aligned with policy. You'll also harden the system into a resilient architecture then see it pivot seamlessly across domains from legal compliance to strategic marketing proving its domain independence.</p><p>By the end of this book you'll be equipped with the skills needed to engineer an adaptable verifiable architecture you can repurpose across domains and deploy with confidence.</p><p><strong>What You Will Learn:</strong></p><p>- Develop memory models to retain short-term and cross-session context</p><p>- Craft semantic blueprints and drive multi-agent orchestration with MCP</p><p>- Implement high-fidelity RAG pipelines with verifiable citations</p><p>- Apply safeguards against prompt injection and data poisoning</p><p>- Enforce moderation and policy-driven control in AI workflows</p><p>- Repurpose the Context Engine across legal marketing and beyond</p><p>- Deploy a scalable observable Context Engine in production</p><p><strong>Who this book is for:</strong></p><p>This book is for AI engineers software developers system architects and data scientists who want to move beyond ad hoc prompting and learn how to design structured transparent and context-aware AI systems. It will also appeal to ML engineers and solutions architects with basic familiarity with LLMs who are eager to understand how to orchestrate agents integrate memory and retrieval and enforce safeguards.</p><p><strong>Table of Contents</strong></p><p>- The Semantic Blueprint: From Prompt to Context</p><p>- The Interactive Architect: Shaping AI Understanding in Real Time</p><p>- Building the Context Library: Programmatic RAG for Reusable Assets</p><p>- Architecting and Debugging the Context Engine</p><p>- Optimizing the Engine: Managing Token Limits and Contextual Quality</p><p>- Use Case 1: Building the Trustworthy Domain-Expert</p><p>- Use Case 2: The Automated Brand Ambassador</p><p>- Use Case 3: The Proactive Support Agent</p><p>- Use Case 4: The Autonomous Orchestrator</p><p>- The Future of Context: Multi-Agent Systems and Evolving Memory</p>