  As more and more AI agents are used in practice, it is time to think about
how to make these agents fully autonomous so that they can (1) learn by
themselves continually in a self-motivated and self-initiated manner rather
than being retrained offline periodically on the initiation of human engineers
and (2) accommodate or adapt to unexpected or novel circumstances. As the
real-world is an open environment that is full of unknowns or novelties, the
capabilities of detecting novelties, characterizing them,
accommodating/adapting to them, gathering ground-truth training data and
incrementally learning the unknowns/novelties become critical in making the AI
agent more and more knowledgeable, powerful and self-sustainable over time. The
key challenge here is how to automate the process so that it is carried out
continually on the agent's own initiative and through its own interactions with
humans, other agents and the environment just like human on-the-job learning.
This paper proposes a framework (called SOLA) for this learning paradigm to
promote the research of building autonomous and continual learning enabled AI
agents. To show feasibility, an implemented agent is also described.
