Winter Is Over for Continual Learning: From Continual Representation Learning to Continual Learning Agents

Continual Learning (CL) rose to prominence with the rise of deep architectures, and then entered a winter as large language models (LLMs) gave the impression that learning over time was no longer needed. We argue that this winter was not a failure but, in fact, a necessary transition. Intelligence cannot be separated from the time in which an agent lives and acts, and the arrival of agentic systems built on large models returns CL to its proper place: not the narrow question of how representations are formed, but the principle by which agents organise their knowledge as its environment changes. We call these systems Continual Learning Agents, designed from the start for adaptation and consolidating what they learn. We follow this path from the early ideas about machines that learn over time, through the era of deep CL, towards an holistic view in which learning continuously and the design of the agent can no longer be held apart, and in which the hard problems of Artificial Intelligence (AI) and those of CL are seen to converge.