From Intelligence to Autonomy: Foundations and Construction of Autonomous Agents for Future Intelligent Systems
Artificial intelligence is moving from one-off perception and generation tasks toward long-term planning, tool use, and interaction with real environments. Greater task competence, however, does not automatically yield autonomy: an external actor may still have to set goals, sustain operating conditions, detect failures, and approve updates. We define an autonomous agent as a system that, within explicit identity and authority boundaries, maintains both task goals and viability conditions, acts continuously using models of the world and itself, and adjusts its control mechanisms and long-term capabilities through feedback. We trace the conceptual path from homeostasis and feedback control to autopoiesis and biological autonomy. We then distinguish six lines of operationalization: computational autopoiesis, behavior-based robotics, agent formalization, adaptive software, embodied adaptation, and robotic self-modeling. On this basis, we propose a one-boundary, four-loop framework in which Operational, Viability, Reflexive, and Learning loops organize task execution, self-maintenance, metacontrol, and governed learning. Five paired simulation experiments test permission constraints, road-closure response, health maintenance, capability re-estimation, and cross-task learning. Their results provide preliminary evidence for the feasibility of these mechanisms, while broader environments and physical systems remain to be tested.