  Leveraging multiple large language model (LLM) agents has shown to be a
promising approach for tackling complex tasks, while the effective design of
multiple agents for a particular application remains an art. It is thus
intriguing to answer a critical question: Given a task, how can we build a team
of LLM agents to solve it effectively? Our new adaptive team-building paradigm
offers a flexible solution, realized through a novel agent design named Captain
Agent. It dynamically forms and manages teams for each step of a task-solving
process, utilizing nested group conversations and reflection to ensure diverse
expertise and prevent stereotypical outputs. It allows for a flexible yet
structured approach to problem-solving and can help reduce redundancy and
enhance output diversity. A comprehensive evaluation across six real-world
scenarios demonstrates that Captain Agent significantly outperforms existing
multi-agent methods with 21.94% improvement in average accuracy, providing
outstanding performance without requiring task-specific prompt engineering.
