Building Applications with AI Agents
Designing and Implementing Multi-Agent Systems
Published on 30. September 2025
Book
Paperback/Softback
300 pages
978-1-0981-7650-1 (ISBN)
Description
Generative AI has revolutionized how organizations tackle problems, accelerating the journey from concept to prototype to solution. While these applications enhance efficiency, they often require extensive planning, drafting, and revising to complete complex tasks. By combining many of these actions, AI agents offer greater autonomy and efficiency, but understanding and deploying them remains a challenge for many organizations, especially as technology and research rapidly develops.This book is your indispensable guide through this intricate and fast-moving landscape. Author Michael Albada provides a practical and research-based approach to designing and implementing single- and multi-agent systems. It simplifies the complexities and equips you with the tools to move from concept to solution efficiently. By the end, you'll:
Understand the distinct features of foundation model-enabled AI agents
Discover the core components and design principles of AI agents
Explore design trade-offs and implement effective multi-agent systems
Design and deploy tailored AI solutions, enhancing efficiency and innovation in your field
Understand the distinct features of foundation model-enabled AI agents
Discover the core components and design principles of AI agents
Explore design trade-offs and implement effective multi-agent systems
Design and deploy tailored AI solutions, enhancing efficiency and innovation in your field
More details
Language
English
Place of publication
Sebastopol
United States
Dimensions
Height: 231 mm
Width: 180 mm
Thickness: 19 mm
Weight
624 gr
ISBN-13
978-1-0981-7650-1 (9781098176501)
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
Person
Michael Albada is a machine learning engineer with nine years of experience designing, building, and deploying large-scale machine learning solutions at Uber, ServiceNow, and Microsoft, with experience in recommendation systems, geospatial modeling, cybersecurity, natural language processing, large language models, and the development of large scale multi-agent systems for cybersecurity. He received his B.A. from Stanford University, M.Phil. from the University of Cambridge, and M.S. from Georgia Tech with a concentration in machine learning.