Agentic AI Alignment

Principles, Methods, and Deployment of Responsible Autonomous Systems
Erscheinungsjahr 2027
ISBN: 978-981-9277-10-0
Verlag: Springer

Buch, Englisch, Format (B × H): 155 mm x 235 mm

Reihe: Foundation Models and Intelligent Systems

As artificial intelligence evolves from systems that primarily generate predictions and responses to autonomous agents capable of planning, maintaining memory, using tools, interacting with people and other agents, and acting in dynamic environments, alignment becomes a systems-level challenge. This book provides a comprehensive treatment of agentic AI alignment, examining how the goals, decisions, actions, and outcomes of autonomous agents can remain consistent with relevant human intentions, values, and constraints throughout their operation.

Bridging foundational concepts with responsible system design and deployment, the book examines how planning, memory, tool use, human interaction, and multi-agent collaboration shape alignment throughout an agent’s operation. It considers fairness, explainability, privacy and security, and human-centered design as interconnected dimensions of responsible agentic AI, while also addressing the practical challenges of monitoring, runtime assurance, auditing, accountability, and governance. Real-world case studies in healthcare, finance, and public services and marine environments illustrate how these considerations change as autonomous agents move from generating information to taking increasingly consequential actions.

Written for AI researchers, graduate students, engineers, practitioners, and policymakers, the book combines conceptual foundations, technical methods, evaluation frameworks, and practical considerations for designing, evaluating, and deploying responsible autonomous systems. Readers will develop a systems-level understanding of the distinctive alignment challenges posed by agentic AI, learn how alignment can be considered across the trajectory from goals and plans to actions and consequences, and explore emerging directions involving self-improving agents, multi-agent ecosystems, collective intelligence, and responsible AGI. A basic background in artificial intelligence and machine learning is recommended, but no prior expertise in AI alignment is required.

Zielgruppe

Research

Autoren/Hrsg.

Zhang, Wenbin

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

Chapter 1 Introduction to Agentic AI Alignment.- Chapter 2 Foundations of Agentic AI Alignment.- Chapter 3 Fairness in Agentic AI.- Chapter 4 Explainable Agentic AI.- Chapter 5 Privacy and Security for Agentic AI.- Chapter 6 Human-Centered Agentic AI.- Chapter 7 Deployment and Governance.- Chapter 8 Applications and Case Studies.- Chapter 9 Future Directions.

Über Autor(innen)

Dr. Wenbin Zhang is an Assistant Professor in the Knight Foundation School of Computing and Information Sciences at Florida International University and an Associate Member of the Te Ipu o Te Mahara Artificial Intelligence Institute. His research focuses on trustworthy and responsible AI, with particular interests in aligning intelligent and autonomous systems with human values and societal goals. His work spans fairness, reliability, and responsible decision-making, with applications in healthcare, digital forensics, finance, energy, transportation, and public policy. Dr. Zhang’s research has been supported by major federal funding, including NIH R01 and NSF CRII awards. He was invited to deliver a New Faculty Highlights talk at AAAI’24 and was named to the Stanford/Elsevier Top 2% Scientists List. His research has received best paper awards and best paper candidate recognitions at ECML PKDD’25, ACM FAccT’23, ICDM’23, WIREs Data Mining and Knowledge Discovery, and ICDM’21. He also contributes extensively to the AI research community through conference leadership and editorial service, including membership on the ECML PKDD Steering Committee, serving as Bridge Chair for AAAI’27, Associate Editor for ACM Computing Surveys, and Action Editor for Machine Learning.