Multi-Agent AI Engineering
Design, build, and operate AI systems that think and act as coordinated teams
Will be published approx. on 30. June 2026
Book
Paperback/Softback
978-1-80669-087-9 (ISBN)
Description
Move agentic AI from clever demos to reliable production with the principles, patterns, and practices behind multi-agent systems at scale.
Key Features
Build production-ready agents and multi-agent systems with hands-on Python examples
Apply foundational principles, proven design patterns, and orchestration strategies
Evaluate, observe, scale, and evolve agent systems through real-world case studies
Purchase of the print or Kindle book includes a free PDF eBook
Book DescriptionAs AI systems take on more complex tasks, the limits of single-model applications become increasingly clear. Problems requiring long-horizon reasoning, specialized expertise, coordination, and parallel execution demand multiple agents working together reliably in production.
But building multi-agent systems is fundamentally an engineering challenge. Agents must communicate, delegate tasks, manage context, recover from failures, and stay aligned on shared goals under real-world constraints.
Multi-Agent AI Engineering is a practical guide to designing and operating production-grade multi-agent systems. Drawing on the authors' research, open-source contributions, and experience building AI systems at scale, the book focuses on architectural principles that extend beyond any single framework or trend.
You'll explore agent foundations, communication protocols, memory and context management, orchestration, interoperability standards, and canonical multi-agent patterns through hands-on Python examples. The book also covers production realities including evaluation, observability, reliability, safe self-improvement, and scaling agentic systems in practice.
By the end, you'll be equipped to design, build, and scale reliable multi-agent systems for real-world deployment.What you will learn
Apply foundational principles to design production-ready agents
Design agent communication, routing, and collaboration flows
Orchestrate teams with proven multi-agent design patterns
Manage memory, retrieval, and context across agent teams
Evaluate, red-team, and benchmark agent system behaviors
Deploy and scale multi-agent systems in production
Instrument agents with OpenTelemetry-based observability
Evolve and improve agent systems safely in production
Who this book is forIf you are an AI engineer, ML practitioner, software architect, or technical leader who wants to move beyond agent demos and ship multi-agent AI systems that work in production, this book is for you. By the end, you will be able to design, deploy, evaluate, and continuously improve agentic systems with confidence. It is equally valuable for engineering and product managers making informed decisions about agentic AI architecture. Readers should be comfortable with Python and have basic familiarity with LLMs; deep ML expertise is not required.
Key Features
Build production-ready agents and multi-agent systems with hands-on Python examples
Apply foundational principles, proven design patterns, and orchestration strategies
Evaluate, observe, scale, and evolve agent systems through real-world case studies
Purchase of the print or Kindle book includes a free PDF eBook
Book DescriptionAs AI systems take on more complex tasks, the limits of single-model applications become increasingly clear. Problems requiring long-horizon reasoning, specialized expertise, coordination, and parallel execution demand multiple agents working together reliably in production.
But building multi-agent systems is fundamentally an engineering challenge. Agents must communicate, delegate tasks, manage context, recover from failures, and stay aligned on shared goals under real-world constraints.
Multi-Agent AI Engineering is a practical guide to designing and operating production-grade multi-agent systems. Drawing on the authors' research, open-source contributions, and experience building AI systems at scale, the book focuses on architectural principles that extend beyond any single framework or trend.
You'll explore agent foundations, communication protocols, memory and context management, orchestration, interoperability standards, and canonical multi-agent patterns through hands-on Python examples. The book also covers production realities including evaluation, observability, reliability, safe self-improvement, and scaling agentic systems in practice.
By the end, you'll be equipped to design, build, and scale reliable multi-agent systems for real-world deployment.What you will learn
Apply foundational principles to design production-ready agents
Design agent communication, routing, and collaboration flows
Orchestrate teams with proven multi-agent design patterns
Manage memory, retrieval, and context across agent teams
Evaluate, red-team, and benchmark agent system behaviors
Deploy and scale multi-agent systems in production
Instrument agents with OpenTelemetry-based observability
Evolve and improve agent systems safely in production
Who this book is forIf you are an AI engineer, ML practitioner, software architect, or technical leader who wants to move beyond agent demos and ship multi-agent AI systems that work in production, this book is for you. By the end, you will be able to design, deploy, evaluate, and continuously improve agentic systems with confidence. It is equally valuable for engineering and product managers making informed decisions about agentic AI architecture. Readers should be comfortable with Python and have basic familiarity with LLMs; deep ML expertise is not required.
More details
Language
English
Place of publication
Birmingham
United Kingdom
Dimensions
Height: 235 mm
Width: 191 mm
ISBN-13
978-1-80669-087-9 (9781806690879)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Xiao Ma is an engineering executive with 15+ years of experience in ML/AI systems across academia and industry. He holds a Ph.D. in Computer Science from the University of Illinois, specialized in the intersection of machine learning and computer systems. As Chief Architect at Pattern Insight and Medium, he led teams developing large-scale ML systems for both enterprise and consumer markets. Currently at Splunk, a Cisco Company, he leads teams building Splunk Observability Cloud, a full-stack solution (connecting infrastructure, applications, and business impact) featuring enterprise-class multi-agent AI systems and industry-leading AI Observability products that empower customers to innovate with confidence at scale.
Chi Wang is the creator of AutoGen, AG2, MassGen, Sutando - open-source projects for agentic AI used by Nvidia, Google, Microsoft, and leading research institutions worldwide. He previously led agentic AI work as a Senior Staff Research Scientist at Google DeepMind, pioneered agentic AI research at Microsoft Research, and created FLAML for AutoML. He teaches at Stanford, Berkeley, Coursera, and DeepLearning.AI. His work has earned the UIUC Siebel School Early Career Alumni Achievement Award, Best Paper at the ICLR'24 LLM Agents Workshop, and the SIGKDD PhD Dissertation Award.
Chi Wang is the creator of AutoGen, AG2, MassGen, Sutando - open-source projects for agentic AI used by Nvidia, Google, Microsoft, and leading research institutions worldwide. He previously led agentic AI work as a Senior Staff Research Scientist at Google DeepMind, pioneered agentic AI research at Microsoft Research, and created FLAML for AutoML. He teaches at Stanford, Berkeley, Coursera, and DeepLearning.AI. His work has earned the UIUC Siebel School Early Career Alumni Achievement Award, Best Paper at the ICLR'24 LLM Agents Workshop, and the SIGKDD PhD Dissertation Award.
Content
Table of Contents
Introduction to Multi-Agent Systems
Principles of Multi-Agent Systems
Frameworks and Mental Models
Constructing Your First Agents
Agent Communication and Collaboration
Context Management in Agents
Orchestrating Agent Teams
Unified Abstractions and Protocols for Agent Collaboration
Design Patterns for Multi-Agent Collaboration
Comparative Survey of Frameworks
Evaluating the Performance and Behaviors of Multi-Agent Systems
Deploying and Scaling Multi-Agent Systems
Observability for Agentic AI
Case Studies from Real-World Applications
Conclusions and Future Outlook
Introduction to Multi-Agent Systems
Principles of Multi-Agent Systems
Frameworks and Mental Models
Constructing Your First Agents
Agent Communication and Collaboration
Context Management in Agents
Orchestrating Agent Teams
Unified Abstractions and Protocols for Agent Collaboration
Design Patterns for Multi-Agent Collaboration
Comparative Survey of Frameworks
Evaluating the Performance and Behaviors of Multi-Agent Systems
Deploying and Scaling Multi-Agent Systems
Observability for Agentic AI
Case Studies from Real-World Applications
Conclusions and Future Outlook