Toward Pre-Deployment Assurance for Enterprise AI Agents: Ontology-Grounded Simulation and Trust Certification
Thanh Luong Tuan, Abhijit Sanyal
Why It Matters
What makes this one worth your time
As AI agents become integral to various industries, ensuring their compliance and safety before deployment is crucial for regulatory adherence and risk management.
A novel framework for ensuring the safe deployment of enterprise AI agents through ontology-based verification.
Summary
The paper presents an ontology-grounded verification framework for pre-deployment assurance of enterprise AI agents, combining an operational envelope, scenario generation pipeline, and a machine-verifiable Trust Certificate, validated across multiple regulated industries.
Key contributions
- Development of an Agent Operational Envelope that formalizes certification across various constraints.
- Creation of an ontology-to-scenario generation pipeline for automatic test scenario derivation.
- Introduction of a machine-verifiable Trust Certificate with graduated deployment verdicts.
Notable insights
- The combination of an operational envelope with an ontology-to-scenario generation pipeline is a unique approach that enhances regulatory coverage.
- The use of graduated deployment verdicts in the Trust Certificate provides a structured method for assessing AI agent readiness.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2606.04037v2 Announce Type: replace 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 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.