A June 2026 arXiv paper (abs/2606.04037) details an ontology-grounded verification framework that generates 1,800 regulatory and adversarial scenarios across Fintech, Banking, Insurance, and Healthcare deployments in the US and Vietnam, achieving 48.3% regulatory coverage against 125 primary-source requirements compared to 33.1% for persona-based baselines. The framework defines an Agent Operational Envelope encompassing permissions, domain constraints, safety properties, governance rules, and autonomy levels, then produces machine-verifiable Trust Certificates with verdicts of Approved, Conditional, or Rejected. Cross-validation across Claude Sonnet 4, Qwen 2.5 72B, and Gemma 4 26B confirmed the coverage pattern in 5,400 total scenarios. The study extends prior LLM benchmarking work by NIST SP 800-218 (2023) on secure development practices and the EU AI Act's pre-market conformity requirements for high-risk systems, exposing gaps in post-deployment monitoring that the ontology pipeline addresses through automated derivation of test cases from regulatory text rather than manual prompt engineering. Coverage gains held after Bonferroni correction only for domain specificity (4.77/5.0), indicating the method's strength lies in constraint formalization over raw volume. Three LLM families replicated the ontology advantage, aligning with patterns observed in DARPA's Explainable AI program reports on verifiable agent behavior in constrained domains.