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

The pre-deployment verification of artificial intelligence (AI) agents in companies remains a critical gap between assessing the capabilities of language models and their deployment in production. Post-implementation monitoring and security controls offer limited assurance once the agent is operational. To address this issue, a verification framework based on ontologies is proposed, combining three components: a formalized certification space, a scenario generation pipeline, and a trust certificate with machine-verifiable testimony. A controlled pilot study in four heavily regulated industries (fintech, banking, insurance, and healthcare) generated 1,800 scenarios for evaluation against 125 regulatory requirements and 25 injected failures. The ontology-based generation achieved a regulatory coverage rate of 48.3% compared to 33.1% for the human-based approach. This demonstrates that ontology-based scenario generation is a credible complement to human-led testing suites in highly regulated domains. This news is significant because it highlights the need to ensure the security and reliability of AI agents in business environments, and how solutions like ontology-based scenario generation can help address this challenge. Furthermore, in the context of AI adoption in commerce, it is essential to have reliable and secure solutions, such as those developed by dataqbs on its open-garage platform.

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