AGENTIC-SYSTEMSJune 4, 20261 min read8 views

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

A new study proposes an ontology-grounded verification framework to comprehensively evaluate AI agents before production deployment. Pilot testing in the US and Vietnam demonstrates its superiority in ensuring regulatory compliance and system safety.

Computer Science > Artificial Intelligence

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

View PDF HTML (experimental)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.

Source: arXiv cs.AI Recent

More in this category

agentic-systems

Align AI to Dynamic Human-AI Workflows

Current AI alignment methods fall short by relying on static representations of human preferences. A new paper proposes a paradigm shift toward "interactive and complementary alignment," allowing AI and humans to co-evolve within dynamic workflows.

agentic-systems

MemoHarness: Agent Harnesses That Learn from Experience

Researchers introduce MemoHarness, an adaptive harness optimization framework that enables AI agents to learn from their own executions. By analyzing performance and storing insights in a dual-layer experience bank, it dynamically adapts to new tasks, outperforming static configurations.

agentic-systems

How Artificial Intelligence LLM Engines Shape the Global Conflict Information Environment

A new study reveals how AI-powered answer engines are highly vulnerable to hallucination and manipulation when reporting on global conflicts. The lack of robust digital records allows malicious actors to exploit these systems through Generative Engine Optimization (GEO), posing a new threat of AI-driven information warfare.

agentic-systems

AI Agents Do Not Fail Alone:The Context Fails First

A new study validates context-engineering quality as an independent leading indicator of AI agent reliability, introducing an open-source evaluation infrastructure called ProofAgent-Harness to predict behavioral outcomes before deployment.

agentic-systems

Interpretable Language Model for Closed-Loop Type 1 Diabetes Control

Researchers have developed LLM-T1D, a novel approach combining reinforcement learning and large language models to automate insulin delivery for Type 1 Diabetes. The system not only outperforms traditional methods but also explains its decisions in plain language, addressing the 'black-box' challenge in medical AI.

agentic-systems

RegNetAgents: A Multi-Agent Framework for Cross-Network Regulatory Driver Identification in Cancer Genomics

Researchers have introduced RegNetAgents, an AI-oriented multi-agent framework designed to identify regulatory driver candidates across heterogeneous gene networks in cancer genomics. By integrating bulk tumor and single-cell data, the system offers a powerful tool for discovering therapeutic targets in breast and colorectal cancers.