Toward Pre-Deployment Assurance for Enterprise AI Agents: Ontology-Grounded Simulation and Trust Certification
Quick Answer
This paper shows that A novel ontology-grounded verification framework for enterprise AI agents enhances pre-deployment assurance, achieving 48.3% regulatory coverage compared to 33.1% for persona-based methods.
Quick Take
Tested across Fintech, Banking, Insurance, and Healthcare, it generated 1,800 scenarios against 125 regulatory requirements.
Key Points
- Framework includes Agent Operational Envelope, scenario generation, and Trust Certificate.
- Pilot study involved 1,800 scenarios across four regulated industries in the US and Vietnam.
- Ontology-grounded generation outperformed persona-based methods in regulatory coverage.
- Cross-validation with three families confirmed the effectiveness of the approach.
- Results suggest a robust method for regulatory-intensive AI deployment.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 04037v1 Announce Type: new Abstract: Pre-deployment verification of enterprise artificial intelligence (AI) agents remains a critical gap between (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.
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