Automated Design of Agentic Systems

• URL: http://arxiv.org/abs/2408.08435v1
• Date: Thu, 15 Aug 2024 21:59:23 GMT
• Title: Automated Design of Agentic Systems
• Authors: Shengran Hu, Cong Lu, Jeff Clune,
• Abstract summary: We formulate a new research area, Automated Design of Agentic Systems, which aims to automatically create powerful agentic system designs. We show that our algorithm can progressively invent agents with novel designs that greatly outperform state-of-the-art hand-designed agents.
• Score: 5.404186221463082
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• AgentSquare: Automatic LLM Agent Search in Modular Design Space [16.659969168343082]
  Large Language Models (LLMs) have led to a rapid growth of agentic systems capable of handling a wide range of complex tasks. We introduce a new research problem: Modularized LLM Agent Search (MoLAS)
  arXiv  Detail & Related papers  (2024-10-08T15:52:42Z)
• Gödel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement [117.94654815220404]
  G"odel Agent is a self-evolving framework inspired by the G"odel machine. G"odel Agent can achieve continuous self-improvement, surpassing manually crafted agents in performance, efficiency, and generalizability.
  arXiv  Detail & Related papers  (2024-10-06T10:49:40Z)
• AutoGen Studio: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems [31.113305753414913]
  AUTOGEN STUDIO is a no-code developer tool for rapidly prototyping multi-agent systems. It provides an intuitive drag-and-drop UI for agent specification, interactive evaluation, and a gallery of reusable agent components.
  arXiv  Detail & Related papers  (2024-08-09T03:27:37Z)
• Agent-E: From Autonomous Web Navigation to Foundational Design Principles in Agentic Systems [1.079505444748609]
  We present our work on building a novel web agent, Agent-E. Agent-E introduces numerous architectural improvements over prior state-of-the-art web agents. We show that Agent-E beats other SOTA text and multi-modal web agents on this benchmark in most categories by 10-30%.
  arXiv  Detail & Related papers  (2024-07-17T21:44:28Z)
• Symbolic Learning Enables Self-Evolving Agents [55.625275970720374]
  We introduce agent symbolic learning, a systematic framework that enables language agents to optimize themselves on their own. Agent symbolic learning is designed to optimize the symbolic network within language agents by mimicking two fundamental algorithms in connectionist learning. We conduct proof-of-concept experiments on both standard benchmarks and complex real-world tasks.
  arXiv  Detail & Related papers  (2024-06-26T17:59:18Z)
• AgentGym: Evolving Large Language Model-based Agents across Diverse Environments [116.97648507802926]
  Large language models (LLMs) are considered a promising foundation to build such agents. We take the first step towards building generally-capable LLM-based agents with self-evolution ability. We propose AgentGym, a new framework featuring a variety of environments and tasks for broad, real-time, uni-format, and concurrent agent exploration.
  arXiv  Detail & Related papers  (2024-06-06T15:15:41Z)
• KwaiAgents: Generalized Information-seeking Agent System with Large Language Models [33.59597020276034]
  Humans excel in critical thinking, planning, reflection, and harnessing available tools to interact with and interpret the world. Recent advancements in large language models (LLMs) suggest that machines might also possess the aforementioned human-like capabilities. We introduce KwaiAgents, a generalized information-seeking agent system based on LLMs.
  arXiv  Detail & Related papers  (2023-12-08T08:11:11Z)
• ProAgent: From Robotic Process Automation to Agentic Process Automation [87.0555252338361]
  Large Language Models (LLMs) have emerged human-like intelligence. This paper introduces Agentic Process Automation (APA), a groundbreaking automation paradigm using LLM-based agents for advanced automation. We then instantiate ProAgent, an agent designed to craft from human instructions and make intricate decisions by coordinating specialized agents.
  arXiv  Detail & Related papers  (2023-11-02T14:32:16Z)
• Agents: An Open-source Framework for Autonomous Language Agents [98.91085725608917]
  We consider language agents as a promising direction towards artificial general intelligence. We release Agents, an open-source library with the goal of opening up these advances to a wider non-specialist audience.
  arXiv  Detail & Related papers  (2023-09-14T17:18:25Z)
• The Rise and Potential of Large Language Model Based Agents: A Survey [91.71061158000953]
  Large language models (LLMs) are regarded as potential sparks for Artificial General Intelligence (AGI) We start by tracing the concept of agents from its philosophical origins to its development in AI, and explain why LLMs are suitable foundations for agents. We explore the extensive applications of LLM-based agents in three aspects: single-agent scenarios, multi-agent scenarios, and human-agent cooperation.
  arXiv  Detail & Related papers  (2023-09-14T17:12:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.