Position: Towards a Responsible LLM-empowered Multi-Agent Systems

• URL: http://arxiv.org/abs/2502.01714v1
• Date: Mon, 03 Feb 2025 16:04:30 GMT
• Title: Position: Towards a Responsible LLM-empowered Multi-Agent Systems
• Authors: Jinwei Hu, Yi Dong, Shuang Ao, Zhuoyun Li, Boxuan Wang, Lokesh Singh, Guangliang Cheng, Sarvapali D. Ramchurn, Xiaowei Huang,
• Abstract summary: The rise of Agent AI and Large Language Model-powered Multi-Agent Systems (LLM-MAS) has underscored the need for responsible and dependable system operation. These advancements introduce critical challenges: LLM agents exhibit inherent unpredictability, and uncertainties in their outputs can compound, threatening system stability. To address these risks, a human-centered design approach with active dynamic moderation is essential.
• Score: 22.905804138387854
• License:
• Abstract: The rise of Agent AI and Large Language Model-powered Multi-Agent Systems (LLM-MAS) has underscored the need for responsible and dependable system operation. Tools like LangChain and Retrieval-Augmented Generation have expanded LLM capabilities, enabling deeper integration into MAS through enhanced knowledge retrieval and reasoning. However, these advancements introduce critical challenges: LLM agents exhibit inherent unpredictability, and uncertainties in their outputs can compound across interactions, threatening system stability. To address these risks, a human-centered design approach with active dynamic moderation is essential. Such an approach enhances traditional passive oversight by facilitating coherent inter-agent communication and effective system governance, allowing MAS to achieve desired outcomes more efficiently.

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