  Large language model (LLM) agents have been shown effective on a wide range
of tasks, and by ensembling multiple LLM agents, their performances could be
further improved. Existing approaches employ a fixed set of agents to interact
with each other in a static architecture, which limits their generalizability
to various tasks and requires strong human prior in designing these agents. In
this work, we propose to construct a strategic team of agents communicating in
a dynamic interaction architecture based on the task query. Specifically, we
build a framework named Dynamic LLM-Agent Network ($\textbf{DyLAN}$) for
LLM-agent collaboration on complicated tasks like reasoning and code
generation. DyLAN enables agents to interact for multiple rounds in a dynamic
architecture with inference-time agent selection and an early-stopping
mechanism to improve performance and efficiency. We further design an automatic
agent team optimization algorithm based on an unsupervised metric termed
$\textit{Agent Importance Score}$, enabling the selection of best agents based
on the contribution each agent makes. Empirically, we demonstrate that DyLAN
performs well in both reasoning and code generation tasks with reasonable
computational cost. DyLAN achieves 13.0% and 13.3% improvement on MATH and
HumanEval, respectively, compared to a single execution on GPT-35-turbo. On
specific subjects of MMLU, agent team optimization in DyLAN increases accuracy
by up to 25.0%.
