Toward Pre-Deployment Assurance for Enterprise AI Agents: Ontology-Grounded Simulation and Trust Certification

Researchers have developed an ontology-grounded verification framework for enterprise AI agents that combines an Agent Operational Envelope, automated scenario generation, and Trust Certificates to provide pre-deployment assurance. A pilot study across four regulated industries in the United States and Vietnam generated 1,800 scenarios and found that ontology-grounded generation achieved 48.3% regulatory coverage compared to 33.1% for persona-based baselines, though the advantage was not robust after statistical correction. The framework addresses a critical gap between LLM capability benchmarking and production deployment by formalizing certification across permissions, domain constraints, safety properties, governance rules, and autonomy levels.

arXiv:2606.04037v1 Announce Type: new Abstract: 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 propose an ontology-grounded verification framework combining 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 Trust Certificate carrying a machine-verifiable attestation with graduated deployment verdicts Approved, Conditional, Rejected . A controlled pilot across four regulated industries Fintech, Banking, Insurance, and Healthcare , instantiated as five industry-by-regulatory-regime cells across the United States and Vietnam, generated 1,800 scenarios evaluated against 125 primary-source regulatory requirements and 25 injected faults. Ontology-grounded generation G4 achieved 48.3% regulatory coverage versus 33.1% for the persona-based baseline corrected p = .0006 and the highest domain specificity 4.77/5.0; p = 2e-6 . The coverage advantage over baseline and retrieval-augmented prompting was not robust after 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 results establish ontology-grounded scenario generation as a credible complement to persona-based test suites for regulatory-intensive domains.