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Context Engineering for Multi-Agent Systems

Context Engineering for Multi-Agent Systems

By : Denis Rothman
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Context Engineering for Multi-Agent Systems

Context Engineering for Multi-Agent Systems

4 (1)
By: Denis Rothman

Overview of this book

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 and apply across real-world scenarios. 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. 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 across domains, from legal compliance to strategic marketing, proving its domain independence. By the end of this book, you’ll be equipped with the skills to engineer an adaptable, verifiable architecture you can repurpose across domains and deploy with confidence. *Email sign-up and proof of purchase required
Table of Contents (16 chapters)
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12
Other Books You May Enjoy
13
Index

Chapter 1

  1. Is the primary goal of context engineering, as defined in this chapter, simply to ask an LLM more creative questions? (Yes or no)

    No. The chapter defines context engineering as providing a structured plan to control and direct the AI’s output, rather than just asking questions.

  2. Does a “Level 2: Linear Context” provide enough information to control an LLM’s narrative style and purpose reliably? (Yes or no)

    No. A linear context improves factual accuracy but does not guide the AI’s style, mood, or purpose, as shown in the examples.

  3. Is the “Semantic Blueprint” at Level 5 presented as the most effective method for architecting a precise and reliable AI response? (Yes or no)

    Yes. The “Semantic Blueprint” is described as the final form of true context architecture, providing a precise and unambiguous plan.

  4. Is the main function of Semantic Role Labeling (SRL) to check the grammatical correctness of a sentence? (Yes or no...
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