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Context Engineering for Multi-Agent Systems: Move beyond prompting to build a Context Engine, a transparent architecture of context and reasoning
396 pages, Paperback
Published November 18, 2025
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December 16, 2025The important points I learned from the book:
1. Structured Context
We cannot rely on unstructured prompts. To improve output reliability, we must engineer the context supplied to the agents. The author calls this structured context a "Blueprint". When dealing with multiple agents, this also means standardizing communication using Model Context Protocol (MCP) to ensure every agent understands the context passed from others.
2. The Evolution of RAG
Copy-pasting text doesn’t scale. We need robust systems to feed context to our agents. The book breaks down Retrieval-Augmented Generation (RAG) into two distinct types:
- Factual RAG: Retrieving static facts from a knowledge base.
- Procedural RAG: Retrieving instructions and procedures to guide the agent's behavior.
3. The Context Engine
To manage these capabilities across multiple agents, we need an orchestration layer: the Context Engine. This engine handles the agentic workflow loop:
- Plan: Determine the necessary steps.
- Execute: Carry out the actions.
- Reflect: Analyze the result and iterate.
Denis Rothman doesn't just discuss theory; he builds it. The book is packed with diagrams and practical code examples that clarify complex concepts. The author also provides sample projects, including a summarizer, a research assistant, and a marketing engine.
Disclaimer: I received a free copy of this book from Packt. However, this review reflects my honest and unbiased opinion.
1. Structured Context
We cannot rely on unstructured prompts. To improve output reliability, we must engineer the context supplied to the agents. The author calls this structured context a "Blueprint". When dealing with multiple agents, this also means standardizing communication using Model Context Protocol (MCP) to ensure every agent understands the context passed from others.
2. The Evolution of RAG
Copy-pasting text doesn’t scale. We need robust systems to feed context to our agents. The book breaks down Retrieval-Augmented Generation (RAG) into two distinct types:
- Factual RAG: Retrieving static facts from a knowledge base.
- Procedural RAG: Retrieving instructions and procedures to guide the agent's behavior.
3. The Context Engine
To manage these capabilities across multiple agents, we need an orchestration layer: the Context Engine. This engine handles the agentic workflow loop:
- Plan: Determine the necessary steps.
- Execute: Carry out the actions.
- Reflect: Analyze the result and iterate.
Denis Rothman doesn't just discuss theory; he builds it. The book is packed with diagrams and practical code examples that clarify complex concepts. The author also provides sample projects, including a summarizer, a research assistant, and a marketing engine.
Disclaimer: I received a free copy of this book from Packt. However, this review reflects my honest and unbiased opinion.
Displaying 1 of 1 review