Runtime Uncertainty Monitoring for LLM-Based Multi-Agent Systems Using Bayesian Networks

This paper investigates how multi-agent systems (MAS)-based on large language models (LLMs) can support actuarial risk modelling, with a particular focus on uncertainty quantification. Actuarial workflows represent a high-stakes decision-support setting where unreliable outputs may lead to incorrect risk assessment, unfair pricing, and regulatory non-compliance. To address uncertainty introduced by the probabilistic nature of LLMs and dependencies between agents, a multi-agent framework is propo

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ethics.ai (28 July 2026), “Runtime Uncertainty Monitoring for LLM-Based Multi-Agent Systems Using Bayesian Networks,” evidence record 14430, https://ethics.ai/record/14430 (originally published by arXiv cs.AI).

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