Autorentext

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.

Klappentext

Generative AI has revolutionized how organizations tackle problems, accelerating the journey from concept to prototype to solution. As the models become increasingly capable, we have witnessed a new design pattern emerge: AI agents. By combining tools, knowledge, memory, and learning with advanced foundation models, we can now sequence multiple model inferences together to solve ambiguous and difficult problems. From coding agents to research agents to analyst agents and more, we've already seen agents accelerate teams and organizations. While these agents enhance efficiency, they often require extensive planning, drafting, and revising to complete complex tasks, 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 multiagent systems. It simplifies the complexities and equips you with the tools to move from concept to solution efficiently.

• 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 multiagent systems
• Design and deploy tailored AI solutions, enhancing efficiency and innovation in your field