Explore/agent app/Toward Pre-Deployment Assurance for Enterprise AI Agents: Ontology-Grounded Simulation and Trust Certification
T

Thanh Luong Tuan, Abhijit Sanyal/Toward Pre-Deployment Assurance for Enterprise AI Agents: Ontology-Grounded Simulation and Trust CertificationUnknown

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.

agent app
GitHubCompare
Refreshed 5d ago
OverviewActivity52wAlternativesDocs
Stars0
Forks0
HF Downloads—30d
Last commit—
Refreshed5d ago
Project healthUnknownNo activity data.
Production readinessResearch / EarlyBest for exploration and prototyping.
Risk notesUnknown licenseVerify license before production use.
AgentHub Score
48 / 100
Composite score from 6 signals. How we score →
Active project
48Score
Growth
40C
Activity
30C
Documentation
70C+
Maturity
45C
Community
42C
Production
58C
GitHub stars · 90 days0 +0.0%
30d90d1y
latest release
Commit activity · 52 weeksActive contributor activity
LowHigh
JunSepDecMarNow
Practical assessment
Should you use it?

✓ Best for

  • Research and experimentation
  • Prototype development
  • Learning agentic patterns

◎ Strengths

  • Active community
  • Open source
  • Well-documented API

✕ Not ideal for

  • Untested at scale without validation
  • Teams without AI/ML expertise

⚠ Watch-outs

  • Review changelog before updating
  • Verify license for commercial use
Technical details
What's inside
Language—
License—
Sourcearxiv
Open source✗ No
Commercial use—
Docs—
Demo—

AgentHub Score

48
Score 48/100
Below average

Alternatives

C
crewai
26.1k · Multi-Agent
87
A
autogen
42.7k · Multi-Agent
71
S
smolagents
11.2k · Coding
84
O
openai-agents-python
9.4k · Multi-Agent
81
Compare all →

Recent activity

Latest commit ——
Indexed by AgentHub crawler5d ago
Monitor for new releasesongoing