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
Michael Albada is a seasoned machine learning engineer with expertise in deploying large-scale solutions for major tech firms including Uber, ServiceNow, and Microsoft. He holds degrees from Stanford University, the University of Cambridge, and Georgia Tech, specializing in machine learning.
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.
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