Agentic artificial intelligence systems introduce a new class of model risk. Unlike traditional predictive models, autonomous agents continuously acquire information, form beliefs regarding latent states of the environment, generate forecasts, select actions, and adapt their behavior over time. Existing validation methodologies focus primarily on predictive accuracy and therefore provide limited insight into the quality of the underlying decision process. This paper proposes a model validation framework for agentic AI based on Partially Observable Markov Decision Processes (POMDPs). The framework decomposes autonomous decision making into information, beliefs, forecasts, actions, and utility, allowing each component to be validated independently. Large language models (LLMs) are formalized as approximate Bayesian filtering operators, and a model-risk taxonomy is developed encompassing state-space, filtering, forecast, policy, utility-specification, and parameter risks. The model risk validation methodology is demonstrated through a portfolio-management case study in which an agent infers latent market regimes from market and macroeconomic information, generates belief-conditioned forecasts, and constructs portfolios using a Black--Litterman framework. Empirical validation combines performance analysis, belief calibration diagnostics, coverage tests, ablation studies, and parameter-sensitivity analysis. The results indicate that latent-state inference contributes independently to decision quality and that the principal conclusions remain robust across a broad range of parameter values. The principal contribution of the paper is a practical framework for extending established model risk management concepts to autonomous AI systems and providing a rigorous foundation for their validation, governance, and monitoring.

翻译：自主式人工智能系统引入了一类新型模型风险。与传统预测模型不同，自主智能体持续获取信息，对环境的潜在状态形成信念，生成预测，选择行动，并随时间调整其行为。现有验证方法主要聚焦于预测准确性，因此对底层决策过程质量的洞察有限。本文提出了一种基于部分可观测马尔可夫决策过程（POMDP）的Agentic人工智能模型验证框架。该框架将自主决策分解为信息、信念、预测、行动和效用五个要素，使每个组件均可独立验证。我们将大语言模型（LLMs）形式化为近似贝叶斯滤波算子，并构建了一个涵盖状态空间、滤波、预测、策略、效用规范及参数风险的模型风险分类体系。通过一个投资组合管理案例研究，我们展示了模型风险验证方法：智能体从市场与宏观经济信息中推断潜在市场状态，生成基于信念的预测，并利用Black--Litterman框架构建投资组合。实证验证综合运用了绩效分析、信念校准诊断、覆盖检验、消融研究和参数敏感性分析。结果表明，潜在状态推断对决策质量具有独立贡献，且主要结论在广泛的参数取值范围内保持稳健。本文的主要贡献在于将既有的模型风险管理概念拓展至自主人工智能系统，构建了一个实用框架，为其验证、治理和监控提供了严谨基础。