Matthew Veach

Date Approved

2025

Degree Type

Open Access Thesis

Degree Name

Master of Science (MS)

Department or School

Information Security and Applied Computing

Committee Member

Tauheed Khan Mohd, Ph.D.

Committee Member

Munther Abualkibash, Ph.D.

Abstract

This thesis presents a cross-sector analysis of agentic AI systems deployed in STEM, education, healthcare, and enterprise domains, with a focus on role suitability, orchestration maturity, and legal accountability. It introduces the Bounded Agentic Suitability Envelope, a dual-bound scoring framework that evaluates deployment viability using weighted assessments of agent capability and decomposed role complexity. Through case studies from Fujitsu, Cleveland Clinic, Carnegie Learning, and Duolingo, the thesis demonstrates measurable gains in efficiency, personalization, and compliance. It argues for an augmentation-first strategy, preserving human roles in high-context domains while enabling targeted replacement in low-complexity workflows. To mitigate risk, the thesis formalizes the Agentic Accountability and Governance Framework, which includes enforceable safeguards such as capability disclosure, intent verification, audit trail retention, role-based access control, and red teaming. AAGF 2.0 is stress-tested against real-world failures, including the DoNotPay litigation, and evaluated for jurisdictional adaptability, liability allocation, and resilience to regulatory capture. While limitations remain (such as reliance on voluntary compliance and lack of empirical validation), the framework provides a legally coherent foundation for enterprise adoption and policy development. This thesis proposes a reproducible model for agentic deployment, balancing innovation with oversight in the emerging AI economy.

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