自主系统的自动设计

Automated Design of Agentic Systems

August 15, 2024

作者: Shengran Hu, Cong Lu, Jeff Clune

cs.AI

摘要

研究人员正在投入大量精力开发功能强大的通用代理，其中基础模型被用作代理系统中的模块（例如“思维链”、“自我反思”、“工具形态”）。然而，机器学习的历史告诉我们，手工设计的解决方案最终会被学习到的解决方案所取代。我们提出了一个新的研究领域，自动设计代理系统（ADAS），旨在自动创建功能强大的代理系统设计，包括发明新的构建模块和/或以新方式组合它们。我们进一步展示了ADAS中一个未被探索但具有前景的方法，其中代理可以用代码定义，并且新代理可以通过元代理编程在代码中自动发现更好的代理。鉴于编程语言是图灵完备的，这种方法在理论上使得学习任何可能的代理系统成为可能：包括新颖的提示、工具使用、控制流以及它们的组合。我们提出了一个简单而有效的算法，名为元代理搜索，来展示这一想法，其中一个元代理迭代地根据先前发现的日益增长的存档编程出有趣的新代理。通过跨多个领域（包括编码、科学和数学）的大量实验，我们展示了我们的算法可以逐步发明具有新设计的代理，这些代理明显优于最先进的手工设计代理。重要的是，我们始终观察到一个令人惊讶的结果，即由元代理搜索发明的代理在跨领域和模型转移时仍然保持出色的性能，展示了它们的稳健性和通用性。只要我们安全地开发它，我们的工作展示了一个激动人心的新研究方向的潜力，即自动设计越来越强大的代理系统以造福人类。

English

Researchers are investing substantial effort in developing powerful general-purpose agents, wherein Foundation Models are used as modules within agentic systems (e.g. Chain-of-Thought, Self-Reflection, Toolformer). However, the history of machine learning teaches us that hand-designed solutions are eventually replaced by learned solutions. We formulate a new research area, Automated Design of Agentic Systems (ADAS), which aims to automatically create powerful agentic system designs, including inventing novel building blocks and/or combining them in new ways. We further demonstrate that there is an unexplored yet promising approach within ADAS where agents can be defined in code and new agents can be automatically discovered by a meta agent programming ever better ones in code. Given that programming languages are Turing Complete, this approach theoretically enables the learning of any possible agentic system: including novel prompts, tool use, control flows, and combinations thereof. We present a simple yet effective algorithm named Meta Agent Search to demonstrate this idea, where a meta agent iteratively programs interesting new agents based on an ever-growing archive of previous discoveries. Through extensive experiments across multiple domains including coding, science, and math, we show that our algorithm can progressively invent agents with novel designs that greatly outperform state-of-the-art hand-designed agents. Importantly, we consistently observe the surprising result that agents invented by Meta Agent Search maintain superior performance even when transferred across domains and models, demonstrating their robustness and generality. Provided we develop it safely, our work illustrates the potential of an exciting new research direction toward automatically designing ever-more powerful agentic systems to benefit humanity.