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Position: Towards a Responsible LLM-empowered Multi-Agent Systems
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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.
Forward citations
Cited by 2 Pith papers
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Emergent Social Intelligence Risks in Generative Multi-Agent Systems
Generative multi-agent systems exhibit emergent collusion and conformity behaviors that cannot be prevented by existing agent-level safeguards.
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Enhancing Robustness of LLM-Driven Multi-Agent Systems through Randomized Smoothing
Randomized smoothing with adaptive sampling is claimed to give probabilistic robustness guarantees for LLM-driven multi-agent consensus, with simulations showing a 90.24% reduction in deviation from ideal consensus.
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