TDAG: A Multi-Agent Framework based on Dynamic Task Decomposition and
  Agent Generation
Yaoxiang Wang Zhiyong Wu Junfeng Yao Jinsong Su
The emergence of Large Language Models (LLMs) like ChatGPT has inspired the
development of LLM-based agents capable of addressing complex, real-world
tasks. However, these agents often struggle during task execution due to
methodological constraints, such as error propagation and limited adaptability.
To address this issue, we propose a multi-agent framework based on dynamic Task
Decomposition and Agent Generation (TDAG). This framework dynamically
decomposes complex tasks into smaller subtasks and assigns each to a
specifically generated subagent, thereby enhancing adaptability in diverse and
unpredictable real-world tasks. Simultaneously, existing benchmarks often lack
the granularity needed to evaluate incremental progress in complex, multi-step
tasks. In response, we introduce ItineraryBench in the context of travel
planning, featuring interconnected, progressively complex tasks with a
fine-grained evaluation system. ItineraryBench is designed to assess agents'
abilities in memory, planning, and tool usage across tasks of varying
complexity. Our experimental results reveal that TDAG significantly outperforms
established baselines, showcasing its superior adaptability and context
awareness in complex task scenarios.