title: Automated Design of Agentic Systems
summary: 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.
paper_authors._id: 66c2a6db198f9d79f20960de
paper_authors.name: Shengran Hu
paper_authors._id: 66c2a6db198f9d79f20960df
paper_authors.name: Cong Lu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-02T08:52:03.275Z
paper_authors._id: 6311ec6ac7722fdac9a93f92
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1670192227454-6311ec6ac7722fdac9a93f92.jpeg
paper_authors.fullname: Cong Lu
paper_authors.type: user
paper_authors.user: conglu
paper_authors._id: 66c2a6db198f9d79f20960e0
paper_authors.name: Jeff Clune
paper_title: Automated Design of Agentic Systems
paper_summary: 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.
paper_ai_keywords: Foundation Models Chain-of-Thought Self-Reflection Toolformer Automated Design of Agentic Systems (ADAS) meta agent Turing Complete Meta Agent Search novel prompts tool use control flows