We develop a simple and straightforward methodology to create AI computer agents that can carry out diverse computer tasks and self-improve by developing tools and augmentations to enable themselves to solve increasingly complex tasks. As large language models (LLMs) have been shown to benefit from non-parametric augmentations, a significant body of recent work has focused on developing software that augments LLMs with various capabilities. Rather than manually developing static software to augment LLMs through human engineering effort, we propose that an LLM agent can systematically generate software to augment itself. We show, through a few case studies, that a minimal querying loop with appropriate prompt engineering allows an LLM to generate and use various augmentations, freely extending its own capabilities to carry out real-world computer tasks. Starting with only terminal access, we prompt an LLM agent to augment itself with retrieval, internet search, web navigation, and text editor capabilities. The agent effectively uses these various tools to solve problems including automated software development and web-based tasks.

翻译：我们提出了一种简单直接的方法，用于创建能够执行多样化计算机任务并实现自我改进的AI计算机智能体：通过开发工具和增强模块，使自身能够解决日益复杂的任务。鉴于大型语言模型（LLMs）已证明能从非参数化增强中受益，近期大量研究聚焦于开发为LLMs赋予各类能力的软件。不同于通过人工工程努力手动开发静态软件来增强LLMs，我们提出一种让LLM智能体系统化生成软件进行自我增强的方案。通过若干案例研究，我们证明：结合适当提示工程的极小查询循环，能使LLM自主生成并运用各类增强模块，自由扩展自身能力以完成实际计算机任务。从仅拥有终端访问权限出发，我们引导LLM智能体通过检索、互联网搜索、网页导航及文本编辑器功能实现自我增强。该智能体能有效运用这些多样化工具解决包括自动化软件开发与基于网页的任务在内的各类问题。