Agentic AI systems in professional domains: A probabilistic framework for role suitability and legal accountability

<p>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.</p>

Name thesis:degree_name

Master of Science (MS)

Level thesis:degree_level

Open Access Thesis

Discipline thesis:degree_discipline

Information Security and Applied Computing

Year dc:date.available

2025

Author dc:creator

• Veach, Matthew

Contributors dc:contributor

• Tauheed Khan Mohd, Ph.D.
• Munther Abualkibash, Ph.D.

• Agentic AI
• Ai Governance
• Ai Risk Managemnet
• Artificial Intelligence
• Legal Framework
• Microsoft Copilot
• Computer Sciences
• Other Computer Sciences

Repository record dc:identifier

https://commons.emich.edu/theses/1326

OAI identifier oai:identifier

oai:commons.emich.edu:theses-2667