Context Engineering for Multi-Agent Systems: Optimizing Memory, Communication, and Workflows in AI Agents

Context Engineering for Multi-Agent Systems: Optimizing Memory, Communication, and Workflow in AI Agents is a practical guide for developers, researchers, and system architects who want to design intelligent, reliable, and scalable agentic AI systems.
Modern AI agents cannot function effectively without well-structured context. From memory persistence to communication protocols, from orchestrating multi-agent workflows to integrating retrieval-augmented knowledge, context engineering is the backbone that determines whether agents collaborate productively-or collapse under complexity.

This book provides a hands-on, implementation-focused framework for mastering context in multi-agent systems. Readers will learn:
How to design short-term and long-term memory architectures for agents.
Strategies for communication across distributed agents, including message passing, routing, and shared state.
Practical approaches to building scalable workflows that integrate both SLMs and LLMs for cost-efficient reasoning.
Methods for connecting vector databases, retrieval-augmented generation (RAG), and external tools into multi-agent pipelines.
Debugging, optimization, and best practices for real-world deployment.
Packed with practical insights, annotated code examples, and actionable patterns, this book bridges the gap between conceptual AI frameworks and production-ready systems. Whether you are an AI developer, researcher, or product engineer, you will gain the skills to optimize memory, communication, and orchestration in your agent workflows.
If you're ready to go beyond surface-level orchestration and master the real engine that drives intelligent agents-contex-this book will give you the blueprint.