Enterprise adoption of Large Language Models (LLMs) is constrained by hallucination, domain drift, and the inability to enforce regulatory compliance at the reasoning level. We present a neurosymbolic architecture implemented within the Foundation AgenticOS (FAOS) platform that addresses these limitations through ontology-constrained neural reasoning. We introduce a three-layer ontological framework--Role, Domain, and Interaction ontologies--grounding LLM-based enterprise agents. We formalize asymmetric neurosymbolic coupling: current enterprise systems constrain agent inputs (context assembly, tool discovery, governance thresholds) but not outputs, and we propose mechanisms extending this coupling to output-side validation (response checking, reasoning verification, compliance enforcement). A controlled experiment (1,800 runs across five industries and three LLMs: Claude Sonnet 4, Qwen 2.5 72B, Gemma 4 26B) finds ontology-coupled agents significantly outperform ungrounded agents on Metric Accuracy (p < .001) and Role Consistency (p < .001) across all three models with large effect sizes (Kendall's W = .46-.64). Improvements are greatest where LLM parametric knowledge is weakest--particularly in Vietnam-localized domains, where ontology lift is 2x that of English domains. Contributions: (1) a formal three-layer enterprise ontology model; (2) a taxonomy of neurosymbolic coupling patterns; (3) ontology-constrained tool discovery via SQL-pushdown scoring; (4) a proposed framework for output-side ontological validation; (5) empirical evidence for the inverse parametric knowledge effect--ontological grounding value is inversely proportional to LLM training-data coverage of the domain; (6) cross-model replication establishing model-independence; (7) a production system serving 22 industry verticals with 650+ agents.

翻译：大型语言模型在企业应用中的推广受制于幻觉、领域偏移以及推理层面无法强制合规约束等问题。我们提出一种神经符号架构，该架构已部署于Foundation AgenticOS（FAOS）平台，通过本体约束的神经推理攻克上述局限。我们引入三层本体框架——角色本体、领域本体与交互本体——以锚定基于LLM的企业智能体。我们形式化定义了非对称神经符号耦合机制：当前企业系统仅约束智能体输入（上下文组装、工具发现、治理阈值）而未能约束输出，为此我们提出将耦合扩展至输出端验证（响应校验、推理验证、合规强制）的机制。一项对照实验（跨5个行业、3种LLM——Claude Sonnet 4、Qwen 2.5 72B、Gemma 4 26B，共执行1,800次）表明，耦合本体的智能体在度量准确度（p<.001）和角色一致性（p<.001）上显著优于无锚定智能体，三种模型均取得大效应量（Kendall W系数=0.46-0.64）。性能提升在LLM参数知识最薄弱领域尤为显著——特别是在越南本地化领域中，本体提升效应是英文领域的两倍。本文贡献包括：（1）形式化三层企业本体模型；（2）神经符号耦合模式分类体系；（3）基于SQL下推评分的本体约束工具发现机制；（4）输出端本体验证框架；（5）逆参数知识效应实证——本体锚定价值与LLM训练数据对领域的覆盖度呈反比；（6）跨模型复现验证彰显模型无关性；（7）支撑22个行业垂直领域、650+智能体的生产系统。