LLMs excel at predictive tasks and complex reasoning tasks, but many high-value deployments rely on decisions under uncertainty, for example, which tool to call, which expert to consult, or how many resources to invest. While the usefulness and feasibility of Bayesian approaches remain unclear for LLM inference, this position paper argues that the control layer of an agentic AI system (that orchestrates LLMs and tools) is a clear case where Bayesian principles should shine. Bayesian decision theory provides a framework for agentic systems that can help to maintain beliefs over task-relevant latent quantities, to update these beliefs from observed agentic and human-AI interactions, and to choose actions. Making LLMs themselves explicitly Bayesian belief-updating engines remains computationally intensive and conceptually nontrivial as a general modeling target. In contrast, this paper argues that coherent decision-making requires Bayesian principles at the orchestration level of the agentic system, not necessarily the LLM agent parameters. This paper articulates practical properties for Bayesian control that fit modern agentic AI systems and human-AI collaboration, and provides concrete examples and design patterns to illustrate how calibrated beliefs and utility-aware policies can improve agentic AI orchestration.

翻译：摘要：大型语言模型在预测任务与复杂推理任务中表现出色，但许多高价值应用场景依赖于不确定条件下的决策，例如调用何种工具、咨询哪位专家或投入多少资源。尽管贝叶斯方法对LLM推理的有效性及可行性尚无定论，本文立场认为，在编排LLM与工具的智能体AI系统控制层中，贝叶斯原理应发挥核心作用。贝叶斯决策理论为智能体系统提供了理论框架，可帮助维护任务相关潜在变量的信念、基于智能体及人机交互观测更新信念，并选择行动。将LLM本身改造为显式贝叶斯信念更新引擎，在计算强度与概念复杂性上仍是通用建模目标的难题。相对而言，本文主张：智能体系统的协调决策需在编排层面应用贝叶斯原理，而非必然涉及LLM代理参数。文章阐明了适用于现代智能体AI系统与人机协作的贝叶斯控制实用属性，并通过具体实例与设计模式展示：校准后的信念与效用感知策略如何优化智能体AI编排效果。