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.

本研究解决了在软件工程领域中，引入基于大型语言模型的多智能体自主系统（LMA）时人机协作中的任务分配信任问题。文章提出了一种基于RACI的框架及其实施指南，旨在提升人机协作效率，确保责任落实，并降低由LLM驱动的自动化可能带来的风险。该框架的实施能够与可信人工智能准则相一致，未来还计划进行实证验证。

促进基于大型语言模型的多智能体系统中的可信人机协作