Pre-deployment verification of enterprise artificial intelligence (AI) agents remains a critical gap between large language model (LLM) capability benchmarking and production deployment. Post-deployment monitoring, human-in-the-loop controls, and prompt-level guardrails offer limited assurance once an agent is operating in production. We present an ontology-grounded verification framework -- to our knowledge the first to combine three components: an Agent Operational Envelope formalizing the certification space across permissions, domain constraints, safety properties, governance rules, and autonomy levels; an ontology-to-scenario generation pipeline that derives regulatory, operational, and adversarial test scenarios automatically; and a machine-verifiable Trust Certificate with graduated deployment verdicts. A controlled pilot across four regulated industries (Fintech, Banking, Insurance, Healthcare), instantiated as five industry-by-regulatory-regime cells across the United States and Vietnam (where Vietnam's 2025 AI Law makes such verification legally mandated for financial services), generated 1,800 scenarios evaluated against 125 primary-source regulatory requirements and 25 injected faults. Ontology-grounded generation significantly outperformed the dominant persona-based baseline on regulatory coverage (48.3% versus 33.1%; corrected p_c = .0006) and attained the highest domain specificity (4.77/5.0; p = 2e-6); transparently, its advantage over plain and retrieval-augmented prompting did not survive Bonferroni correction. Cross-validation across three LLM families (Claude Sonnet 4, Qwen 2.5 72B, Gemma 4 26B; 5,400 total scenarios) replicated the persona-versus-ontology pattern. The framework offers a reproducible, regulation-grounded route to pre-deployment assurance for enterprise AI agents, complementing runtime governance with an auditable deployment gate.

翻译：企业人工智能（AI）智能体的部署前验证，在大语言模型（LLM）能力基准测试与生产部署之间仍存在关键空白。一旦智能体在生产环境中运行，部署后监控、人在回路控制以及提示层级防护栏所提供的保障有限。我们提出了一种基于本体的验证框架——据我们所知，这是首个融合三个组成部分的框架：代理操作包络（Agent Operational Envelope），用于形式化权限、领域约束、安全属性、治理规则及自主性层级构成的认证空间；本体到场景生成流水线，可自动推导出监管、运营和对抗性测试场景；以及包含分级部署鉴定的机器可验证信任证书（Trust Certificate）。在四个受监管行业（金融科技、银行业、保险业、医疗保健）中进行受控试点，具体实例化为横跨美国与越南的五个行业-监管体制组合（越南2025年《人工智能法》强制要求对金融服务进行此类验证），生成了1,800个场景，依据125项首要来源监管要求和25个注入故障进行评估。基于本体的生成方法在监管覆盖率上显著优于主流基于角色的基线方法（48.3%对33.1%；校正后p_c =0.0006），并达到最高领域特异性（4.77/5.0；p =2e-6）；需透明说明的是，其相对于纯提示和检索增强提示的优势未能通过Bonferroni校正检验。跨三个LLM系列（Claude Sonnet 4、Qwen 2.5 72B、Gemma 4 26B；总计5,400个场景）的交叉验证复现了角色方法与本体方法的对比结果。该框架为企业AI智能体提供了可复现且监管驱动的部署前保障路径，通过可审计的部署门控机制补充运行时治理。