  The rapid advancement of neural language models has sparked a new surge of
intelligent agent research. Unlike traditional agents, large language
model-based agents (LLM agents) have emerged as a promising paradigm for
achieving artificial general intelligence (AGI) due to their superior reasoning
and generalization capabilities. Effective planning is crucial for the success
of LLM agents in real-world tasks, making it a highly pursued topic in the
community. Current planning methods typically translate tasks into executable
action sequences. However, determining a feasible or optimal sequence for
complex tasks at fine granularity, which often requires compositing long chains
of heterogeneous actions, remains challenging. This paper introduces Meta-Task
Planning (MTP), a zero-shot methodology for collaborative LLM-based multi-agent
systems that simplifies complex task planning by decomposing it into a
hierarchy of subordinate tasks, or meta-tasks. Each meta-task is then mapped
into executable actions. MTP was assessed on two rigorous benchmarks,
TravelPlanner and API-Bank. Notably, MTP achieved an average $\sim40\%$ success
rate on TravelPlanner, significantly higher than the state-of-the-art (SOTA)
baseline ($2.92\%$), and outperforming $LLM_{api}$-4 with ReAct on API-Bank by
$\sim14\%$, showing the immense potential of integrating LLM with multi-agent
systems.
