Toward Pre-Deployment Assurance for Enterprise AI Agents: Ontology-Grounded Simulation and Trust Certification
This paper proposes an ontology-grounded verification framework for pre-deployment assurance of enterprise AI agents. The framework combines an Agent Operational Envelope, an ontology-to-scenario generation pipeline, and a Trust Certificate. A pilot in four regulated industries across the US and Vietnam generated 1,800 scenarios and showed ontology-grounded generation achieved 48.3% regulatory coverage versus 33.1% for a persona-based baseline, with the highest domain specificity.
[2606.04037] Toward Pre-Deployment Assurance for Enterprise AI Agents: Ontology-Grounded Simulation and Trust Certification
[Submitted on 2 Jun 2026]
Title:Toward Pre-Deployment Assurance for Enterprise AI Agents: Ontology-Grounded Simulation and Trust Certification
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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.
Comments: 26 pages, 3 figures. Companion to arXiv:2604.00555
Subjects:
Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Software Engineering (cs.SE)
ACM classes: I.2.0; D.2.4
Cite as: arXiv:2606.04037 [cs.AI]
(or arXiv:2606.04037v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.04037
arXiv-issued DOI via DataCite
Submission history
From: Thanh Luong Tuan [view email] [v1] Tue, 2 Jun 2026 02:37:11 UTC (62 KB)
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