The Agentic Web: Networked, Continually-Adapting Agent Ecosystems

AI's basic unit of computation is shifting, from single models and individual agents toward continually running, internet-scale collectives of agents that discover one another, communicate, coordinate, and adapt over open networks. Studying these collectives raises questions that single-agent methods do not answer: how to represent and discover agent capabilities at web scale; how learning, credit assignment, and self-improvement behave over changing agent graphs; and how large populations of agents coordinate, remain trustworthy, and stay safe under strategic pressure. These questions are pressing because the architectural and evaluation choices being made now will harden into lasting path dependencies, and no shared benchmarks or evaluation methodology yet exist. This workshop treats the networked agent collective as a first-class research object and convenes researchers across multi-agent and representation learning, learning theory, reinforcement learning, and the societal impacts of AI, through invited and contributed talks, a panel, a hands-on session, and open discussion, to identify the field's foundational problems and the shared evaluation it needs.