  Foundation models (FMs) such as large language models have revolutionized the
field of AI by showing remarkable performance in various tasks. However, they
exhibit numerous limitations that prevent their broader adoption in many
real-world systems, which often require a higher bar for trustworthiness and
usability. Since FMs are trained using loss functions aimed at reconstructing
the training corpus in a self-supervised manner, there is no guarantee that the
model's output aligns with users' preferences for a specific task at hand. In
this survey paper, we propose a conceptual framework that encapsulates
different modes by which agents could interact with FMs and guide them suitably
for a set of tasks, particularly through knowledge augmentation and reasoning.
Our framework elucidates agent role categories such as updating the underlying
FM, assisting with prompting the FM, and evaluating the FM output. We also
categorize several state-of-the-art approaches into agent interaction
protocols, highlighting the nature and extent of involvement of the various
agent roles. The proposed framework provides guidance for future directions to
further realize the power of FMs in practical AI systems.
