  Foundation models, such as large language models (LLMs), have been widely
recognised as transformative AI technologies due to their capabilities to
understand and generate content, including plans with reasoning capabilities.
Foundation model based agents derive their autonomy from the capabilities of
foundation models, which enable them to autonomously break down a given goal
into a set of manageable tasks and orchestrate task execution to meet the goal.
Despite the huge efforts put into building foundation model based agents, the
architecture design of the agents has not yet been systematically explored.
Also, while there are significant benefits of using agents for planning and
execution, there are serious considerations regarding responsible AI related
software quality attributes, such as security and accountability. Therefore,
this paper presents a pattern-oriented reference architecture that serves as
guidance when designing foundation model based agents. We evaluate the
completeness and utility of the proposed reference architecture by mapping it
to the architecture of two real-world agents.
