  Current language model-driven agents often lack mechanisms for effective user
participation, which is crucial given the vagueness commonly found in user
instructions. Although adept at devising strategies and performing tasks, these
agents struggle with seeking clarification and grasping precise user
intentions. To bridge this gap, we introduce Intention-in-Interaction (IN3), a
novel benchmark designed to inspect users' implicit intentions through explicit
queries. Next, we propose the incorporation of model experts as the upstream in
agent designs to enhance user-agent interaction. Employing IN3, we empirically
train Mistral-Interact, a powerful model that proactively assesses task
vagueness, inquires user intentions, and refines them into actionable goals
before starting downstream agent task execution. Integrating it into the XAgent
framework, we comprehensively evaluate the enhanced agent system regarding user
instruction understanding and execution, revealing that our approach notably
excels at identifying vague user tasks, recovering and summarizing critical
missing information, setting precise and necessary agent execution goals, and
minimizing redundant tool usage, thus boosting overall efficiency. All the data
and codes are released.
