​SYNOPSIS

​Modern “agentic” AI systems—LLM-driven agents, tool-using copilots, and autonomous workflows—are moving from curated demos to real deployments that require long-horizon planning, reliable tool use, and robust interaction with people and environments. This symposium will highlight cutting-edge advances in LLM-based agents and lessons learned from building and deploying them.

​We also use today’s agentic AI as a lens on enduring challenges in robotics and embodied AI, and on interaction-rich settings studied in multiagent systems. By linking these perspectives, we aim to surface shared open problems and spark collaborations that move agentic AI toward more reliable, safe, and capable systems.

​DISTINGUISHED SPEAKERS & PANELISTS BIOGRAPHIES

​Agentifying Agentic AI by Prof. Frank DIGNUM, UMEA University

​Prof. Frank Dignum is a Professor and the Wallenberg chair in socially aware AI at Umeå University. His group works on socially aware AI that creates computational models of social aspects such as norms, values, practices, and conventions. These models can be used to create social simulations that are more realistic and give insights into how society will react to changes in policies and natural disasters. They are also used to create more natural dialogues with chatbots that can be used for training medical students to have conversations with patients. He received the best paper award in the AAMAS 2020 Blue Sky track for the paper ``Agents are Dead. Long live Agents!'', a call to action for the community to work on social, responsible, beneficial aspects that make agent technology useful for many application areas.

​Building Rational Robots by Prof. Leslie KAELBLING, Massachusetts Institute of Technology (MIT)

​Prof. Leslie Kaelbling is the Panasonic Professor of Computer Science and Engineering at the Massachusetts Institute of Technology. She is widely recognized for adapting partially observable Markov decision processes from operations research for application in artificial intelligence and robotics, and received the IJCAI Computers and Thought Award in 1997 for applying reinforcement learning to embedded control systems and developing programming tools for robot navigation. She is also the founder of the Journal of Machine Learning Research, a peer-reviewed open access journal.

​Guarding the Future: Advancing Risk Assessment, Safety Alignment, and Guardrails for AI Agents by Assoc. Prof. Bo LI, University of Illinois Urbana-Champaign (UIUC)

​Prof. Bo Li is the Abbasi Associate Professor in the Computer Science Department at the University of Illinois at Urbana-Champaign. She is the recipient of the IJCAI Computers and Thought Award, Alfred P. Sloan Research Fellowship, NSF CAREER Award, AI's 10 to Watch, MIT Technology Review TR-35 Award, Dean's Award for Excellence in Research, C.W. Gear Outstanding Faculty Award, Intel Rising Star Award, Symantec Research Labs Fellowship, Rising Stars in EECS, Research Awards from Tech companies such as Amazon, Meta, Google, Intel, MSR, eBay, and IBM, and best paper awards at several top machine learning and security conferences. Her research focuses on both theoretical and practical aspects of trustworthy machine learning, which is at the intersection of machine learning, security, privacy, and game theory. She has designed several scalable frameworks for robust learning and privacy-preserving data publishing systems, and is also the founder and CEO of Virtue AI.

​Finding supervision for complex tasks by Asst Prof. Pang Wei KOH, University of Washington & Allen Institute for AI

​Prof. Pang Wei Koh is an assistant professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, a visiting research scientist at the Allen Institute for AI, and a Singapore AI Visiting Professor. His research interests are in the theory and practice of building reliable machine learning systems. His research has been published in Nature and Cell, featured in The New York Times and The Washington Post, and recognized by the AI2050 Early Career Fellowship, MIT Tech Review Innovators Under 35 Asia Pacific award, Google ML and Systems Junior Faculty Award, and best paper awards at ICML, KDD, and ACL. He received his PhD in Computer Science from Stanford, advised by Percy Liang. Before that, he was the 3rd employee and Director of Partnerships at Coursera.