Agentic Large Language Models, a survey
Journal:
arXiv
Published Date:
Mar 29, 2025
Abstract
There is great interest in agentic LLMs, large language models that act as
agents. We review the growing body of work in this area and provide a research
agenda. Agentic LLMs are LLMs that (1) reason, (2) act, and (3) interact. We
organize the literature according to these three categories. The research in
the first category focuses on reasoning, reflection, and retrieval, aiming to
improve decision making; the second category focuses on action models, robots,
and tools, aiming for agents that act as useful assistants; the third category
focuses on multi-agent systems, aiming for collaborative task solving and
simulating interaction to study emergent social behavior. We find that works
mutually benefit from results in other categories: retrieval enables tool use,
reflection improves multi-agent collaboration, and reasoning benefits all
categories. We discuss applications of agentic LLMs and provide an agenda for
further research. Important applications are in medical diagnosis, logistics
and financial market analysis. Meanwhile, self-reflective agents playing roles
and interacting with one another augment the process of scientific research
itself. Further, agentic LLMs may provide a solution for the problem of LLMs
running out of training data: inference-time behavior generates new training
states, such that LLMs can keep learning without needing ever larger datasets.
We note that there is risk associated with LLM assistants taking action in the
real world, while agentic LLMs are also likely to benefit society.
