Agentic AI Alignment
Principles, Methods, and Deployment of Responsible Autonomous Systems
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Beschreibung
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.
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.
Connects agentic AI alignment theory with real-world applications in consequential domains Provides practical frameworks for designing and evaluating responsible autonomous agents Examines alignment across goals, plans, actions, and outcomes throughout agent operation
Autor*in
Wenbin Zhang
Themen in »Agentic AI Alignment«
AI alignment AI Responsible AI Trustworthy AI Autonomous Agents AI Safety Multi-Agent Systems AI Governance Frameworks Human-Centered AI Privacy and Security in AI Explainable Agentic AI Agentic AI Alignment