  The rapid advancement and widespread deployment of foundation model (FM)
based systems have revolutionized numerous applications across various domains.
However, the fast-growing capabilities and autonomy have also raised
significant concerns about responsible AI and AI safety. Recently, there have
been increasing attention toward implementing guardrails to ensure the runtime
behavior of FM-based systems is safe and responsible. Given the early stage of
FMs and their applications (such as agents), the design of guardrails have not
yet been systematically studied. It remains underexplored which software
qualities should be considered when designing guardrails and how these
qualities can be ensured from a software architecture perspective. Therefore,
in this paper, we present a taxonomy for guardrails to classify and compare the
characteristics and design options of guardrails. Our taxonomy is organized
into three main categories: the motivation behind adopting runtime guardrails,
the quality attributes to consider, and the design options available. This
taxonomy provides structured and concrete guidance for making architectural
design decisions when designing guardrails and highlights trade-offs arising
from the design decisions.
