  Decision making demands intricate interplay between perception, memory, and
reasoning to discern optimal policies. Conventional approaches to decision
making face challenges related to low sample efficiency and poor
generalization. In contrast, foundation models in language and vision have
showcased rapid adaptation to diverse new tasks. Therefore, we advocate for the
construction of foundation agents as a transformative shift in the learning
paradigm of agents. This proposal is underpinned by the formulation of
foundation agents with their fundamental characteristics and challenges
motivated by the success of large language models (LLMs). Moreover, we specify
the roadmap of foundation agents from large interactive data collection or
generation, to self-supervised pretraining and adaptation, and knowledge and
value alignment with LLMs. Lastly, we pinpoint critical research questions
derived from the formulation and delineate trends for foundation agents
supported by real-world use cases, addressing both technical and theoretical
aspects to propel the field towards a more comprehensive and impactful future.
