自主系統的自動設計

Automated Design of Agentic Systems

August 15, 2024

作者: Shengran Hu, Cong Lu, Jeff Clune

cs.AI

摘要

研究人員正投入大量努力發展功能強大的通用代理，其中基礎模型被用作代理系統中的模組（例如思維鏈、自我反思、工具形塑）。然而，機器學習的歷史告訴我們，手工設計的解決方案最終會被學習得到的解決方案取代。我們提出了一個新的研究領域，自動設計代理系統（ADAS），旨在自動創建功能強大的代理系統設計，包括發明新的構建塊和/或以新方式組合它們。我們進一步展示，在ADAS中存在一種未被探索但具有潛力的方法，其中代理可以用程式碼定義，並且新代理可以通過元代理編程自動發現並不斷改進。考慮到編程語言是圖靈完備的，這種方法在理論上使得學習任何可能的代理系統成為可能：包括新穎的提示、工具使用、控制流程以及它們的組合。我們提出了一種簡單而有效的算法，名為元代理搜索，來展示這個想法，其中一個元代理迭代地基於先前發現的日益增長的存檔來編寫有趣的新代理。通過在包括編碼、科學和數學在內的多個領域進行廣泛實驗，我們展示了我們的算法可以逐步發明具有新設計的代理，這些代理明顯優於最先進的手工設計代理。重要的是，我們一貫觀察到令人驚訝的結果，即由元代理搜索發明的代理在跨領域和模型轉移時仍保持著卓越的性能，展示了它們的穩健性和通用性。只要我們安全地發展它，我們的工作展示了一個引人入勝的新研究方向的潛力，即自動設計功能更強大的代理系統以造福人類。

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.