Proactive AI for Superintelligent Agents
Abstract
AI agents are increasingly capable of reasoning, planning, using tools, and executing long-horizon tasks, yet many still rely on humans to identify worthwhile work and initiate it. Proactiveness adds the capacity to initiate purposeful action without an explicit request. We examine proactiveness as a capability that complements intelligence and alignment in the development of superintelligent agents. Research on proactiveness, however, remains fragmented across communities with different terminology, assumptions, and evaluation practices. We integrate its conceptual foundations and historical development across control, agent theory, ubiquitous computing, mixed-initiative interaction, and foundation-model research. We organize enabling mechanisms into six capacities: awareness, anticipation, agenda formation, arbitration, action, and adaptation. We synthesize evaluation approaches at the task, mechanism, and user levels. Across 13 application domains, including coding, autonomous research, robotics, healthcare, and education, we compare the initiative agents exercise with the authority they are granted. Finally, we outline future directions in capability development, safety and governance, and emerging application frontiers, grounding the development of calibrated proactiveness for superintelligent agents in the achievements and limitations of existing systems.
Research Areas
