This paper talks about how large language models (LLMs) can be used to create agents, which are like computer programs that can think and act for themselves. LLMs are really good at understanding language, but they aren't so good at planning out complicated tasks. The paper explains how to break down big tasks into smaller steps that LLMs can handle, how to give LLMs access to outside information to help them make better decisions, and how to give them special "personas" or roles to play to improve their performance. The authors also discuss ways to handle errors, how to manage the information that LLMs need to remember, and how to evaluate whether an LLM agent is doing its job correctly. The paper emphasizes the importance of thinking like a software engineer when building these agents, combining the strengths of LLMs with traditional programming techniques to create more reliable and effective systems.

https://arxiv.org/pdf/2412.04093