The potential of automatic task-solving through Large Language Model (LLM)-based multi-agent collaboration has recently garnered widespread attention from both the research community and industry. While utilizing natural language to coordinate multiple agents presents a promising avenue for democratizing agent technology for general users, designing coordination strategies remains challenging with existing coordination frameworks. This difficulty stems from the inherent ambiguity of natural language for specifying the collaboration process and the significant cognitive effort required to extract crucial information (e.g. agent relationship, task dependency, result correspondence) from a vast amount of text-form content during exploration. In this work, we present a visual exploration framework to facilitate the design of coordination strategies in multi-agent collaboration. We first establish a structured representation for LLM-based multi-agent coordination strategy to regularize the ambiguity of natural language. Based on this structure, we devise a three-stage generation method that leverages LLMs to convert a user's general goal into an executable initial coordination strategy. Users can further intervene at any stage of the generation process, utilizing LLMs and a set of interactions to explore alternative strategies. Whenever a satisfactory strategy is identified, users can commence the collaboration and examine the visually enhanced execution result. We develop AgentCoord, a prototype interactive system, and conduct a formal user study to demonstrate the feasibility and effectiveness of our approach.

翻译：基于大语言模型（LLM）的多智能体自动任务求解潜力近期受到学术界和工业界的广泛关注。虽然利用自然语言协调多个智能体为普通用户普及智能体技术提供了有前景的途径，但在现有协作框架下设计协调策略仍具有挑战性。这种困难源于自然语言在描述协作过程时固有的歧义性，以及在探索过程中从大量文本内容中提取关键信息（如智能体关系、任务依赖关系、结果对应关系）所需的大量认知负荷。本文提出了一种可视化探索框架，用于促进多智能体协作中协调策略的设计。我们首先为基于LLM的多智能体协调策略建立了结构化表示，以规范自然语言的歧义性。基于此结构，我们设计了三阶段生成方法，利用LLM将用户的一般目标转化为可执行的初始协调策略。用户可在生成过程的任何阶段进行干预，利用LLM和一组交互操作探索替代策略。当找到满意的策略后，用户可以启动协作并检查经过可视化增强的执行结果。我们开发了原型交互系统AgentCoord，并通过正式的用户研究证明了我们方法的可行性和有效性。