  Software agents, both human and computational, do not exist in isolation and
often need to collaborate or coordinate with others to achieve their goals. In
human society, social mechanisms such as norms ensure efficient functioning,
and these techniques have been adopted by researchers in multi-agent systems
(MAS) to create socially aware agents. However, traditional techniques have
limitations, such as operating in limited environments often using brittle
symbolic reasoning. The advent of Large Language Models (LLMs) offers a
promising solution, providing a rich and expressive vocabulary for norms and
enabling norm-capable agents that can perform a range of tasks such as norm
discovery, normative reasoning and decision-making. This paper examines the
potential of LLM-based agents to acquire normative capabilities, drawing on
recent Natural Language Processing (NLP) and LLM research. We present our
vision for creating normative LLM agents. In particular, we discuss how the
recently proposed "LLM agent" approaches can be extended to implement such
normative LLM agents. We also highlight challenges in this emerging field. This
paper thus aims to foster collaboration between MAS, NLP and LLM researchers in
order to advance the field of normative agents.
