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

Abstract: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 proposed in which specialised agents perform data preparation, modelling, review, and explanation tasks under a central hub. The main contribution is a novel approach to uncertainty propagation using token-level log-probabilities and a Bayesian Network. Importantly, log probabilities are not treated as direct probabilities of correctness or task success. Instead, length-normalised log-probability summaries are transformed into calibrated task-level confidence estimates before incorporation into the Bayesian Network. Results show that the framework reproduces baseline actuarial performance while providing additional insight into workflow stability and runtime uncertainty propagation.

Artificial Intelligence (cs.AI)

arXiv:2607.25877 [cs.AI]

(or arXiv:2607.25877v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.25877 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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