Automated Design of Agentic Systems
Authors: name: Shengran Hu, name: Cong Lu, name: Jeff Clune

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.