  Existing LLMs exhibit remarkable performance on various NLP tasks, but still
struggle with complex real-world tasks, even equipped with advanced strategies
like CoT and ReAct. In this work, we propose the CoAct framework, which
transfers the hierarchical planning and collaboration patterns in human society
to LLM systems. Specifically, our CoAct framework involves two agents: (1) A
global planning agent, to comprehend the problem scope, formulate macro-level
plans and provide detailed sub-task descriptions to local execution agents,
which serves as the initial rendition of a global plan. (2) A local execution
agent, to operate within the multi-tier task execution structure, focusing on
detailed execution and implementation of specific tasks within the global plan.
Experimental results on the WebArena benchmark show that CoAct can re-arrange
the process trajectory when facing failures, and achieves superior performance
over baseline methods on long-horizon web tasks. Code is available at
https://github.com/xmhou2002/CoAct.
