  The evolution of large language models (LLMs) has enhanced the planning
capabilities of language agents in diverse real-world scenarios. Despite these
advancements, the potential of LLM-powered agents to comprehend ambiguous user
instructions for reasoning and decision-making is still under exploration. In
this work, we introduce a new task, Proactive Agent Planning, which requires
language agents to predict clarification needs based on user-agent conversation
and agent-environment interaction, invoke external tools to collect valid
information, and generate a plan to fulfill the user's demands. To study this
practical problem, we establish a new benchmark dataset, Ask-before-Plan. To
tackle the deficiency of LLMs in proactive planning, we propose a novel
multi-agent framework, Clarification-Execution-Planning (\texttt{CEP}), which
consists of three agents specialized in clarification, execution, and planning.
We introduce the trajectory tuning scheme for the clarification agent and
static execution agent, as well as the memory recollection mechanism for the
dynamic execution agent. Extensive evaluations and comprehensive analyses
conducted on the Ask-before-Plan dataset validate the effectiveness of our
proposed framework.
