一句话：提出基于RACI的框架，为LLM多智能体软件工程系统分配人机任务，保障可信协作与问责。
将LLM作为自主智能体核心的LLM多智能体自治系统（LMA）正被引入软件工程（SE），但其部署面临诸多挑战，其中关键之一是如何以可信方式在人与LMA系统之间战略性地分配任务。本文为一篇工作进展论文，提出一个基于RACI的框架，并给出相应的实施指南与一个示例实现。该框架旨在促进高效的人机协作、确保问责，并缓解LLM驱动自动化带来的潜在风险，同时与可信AI（Trustworthy AI）准则对齐。文中还描述了后续工作计划，包括拟采用的实证验证方法。
Multi-agent autonomous systems (MAS) are better at addressing challenges that spans across multiple domains than singular autonomous agents. This holds true within the field of software engineering (SE) as well. The state-of-the-art research on MAS within SE focuses on integrating LLMs at the core of autonomous agents to create LLM-based multi-agent autonomous (LMA) systems. However, the introduction of LMA systems into SE brings a plethora of challenges. One of the major challenges is the strategic allocation of tasks between humans and the LMA system in a trustworthy manner. To address this challenge, a RACI-based framework is proposed in this work in progress article, along with implementation guidelines and an example implementation of the framework. The proposed framework can facilitate efficient collaboration, ensure accountability, and mitigate potential risks associated with LLM-driven automation while aligning with the Trustworthy AI guidelines. The future steps for this work delineating the planned empirical validation method are also presented.