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

Researchers propose an ontology-grounded framework for pre-deployment verification of enterprise AI agents, combining formalized operational envelopes with automated regulatory scenario generation and trust certification. A controlled pilot across fintech, banking, insurance, and healthcare found ontology-based testing achieved 48.3% regulatory coverage versus 33.1% for persona-based baselines, establishing a new standard for AI safety assurance in regulated industries.

The research addresses a critical vulnerability in enterprise AI deployment: the gap between LLM capability testing and production safety. Most current safeguards—post-deployment monitoring, human oversight, and prompt-level controls—operate reactively after agents are live. This framework shifts toward proactive verification by formalizing an Agent Operational Envelope that encodes permissions, domain constraints, safety properties, governance rules, and autonomy levels as machine-readable ontologies. The system then automatically generates test scenarios derived from these ontologies rather than relying on human-written personas, yielding a 45% improvement in regulatory coverage.

The research validates this approach through extensive empirical work: 1,800 test scenarios across five regulatory-regime cells in the US and Vietnam, evaluated against 125 primary-source regulatory requirements with intentionally injected faults. Cross-validation across three LLM families (Claude, Qwen, Gemma) strengthens generalizability, though the authors note the coverage advantage loses robustness after Bonferroni correction, indicating the effect size, while statistically significant, requires further validation.

For enterprises deploying AI agents in regulated sectors, this work establishes a credible methodology for demonstrating compliance. The machine-verifiable Trust Certificate with graduated verdicts (Approved, Conditional, Rejected) provides clear deployment gates. The ontology-grounded approach particularly benefits industries like fintech and healthcare where regulatory requirements are dense and domain-specific constraints are non-negotiable. This framework could accelerate enterprise adoption of autonomous AI agents by reducing deployment risk and providing auditable compliance evidence—critical prerequisites for regulated industries to confidently move beyond pilots into production.

• →Ontology-grounded scenario generation achieved 48.3% regulatory coverage versus 33.1% for persona-based testing, establishing a superior methodology for AI safety verification.
• →The framework produces machine-verifiable Trust Certificates with graduated deployment verdicts, creating an auditable compliance layer for regulated industries.
• →Cross-validation across three LLM families demonstrates the approach generalizes across model architectures, not just individual models.
• →Results show domain specificity (4.77/5.0) is significantly higher with ontology-based generation, crucial for fintech, banking, insurance, and healthcare deployment.
• →Post-Bonferroni correction reveals the coverage advantage, while statistically significant, requires further validation before widespread regulatory adoption.