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.
","abstract_html":"<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>","abstract_has_math":false,"creators":["Veach, Matthew"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Open Access Thesis","degree_discipline":"Information Security and Applied Computing","degree_department":null,"school":null,"contributors":["Tauheed Khan Mohd, Ph.D.","Munther Abualkibash, Ph.D."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-01-01T08:00:00Z","date_published":"2025-01-01T08:00:00Z","updated_at":"2026-07-24T02:17:53Z","subjects":["Agentic AI","Ai Governance","Ai Risk Managemnet","Artificial Intelligence","Legal Framework","Microsoft Copilot","Computer Sciences","Other Computer Sciences"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.emich.edu/theses/1326","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Tauheed Khan Mohd, Ph.D.","Munther Abualkibash, Ph.D."]},{"key":"dc:creator","label":"Author","values":["Veach, Matthew"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2026-02-20T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Information Security and Applied Computing"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Open Access Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Agentic AI","Ai Governance","Ai Risk Managemnet","Artificial Intelligence","Legal Framework","Microsoft Copilot","Computer Sciences","Other Computer Sciences"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.emich.edu/theses/1326"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Agentic AI systems in professional domains: A probabilistic framework for role suitability and legal accountability"]}]}],"canonical_facts":{"dc:contributor":["Tauheed Khan Mohd, Ph.D.","Munther Abualkibash, Ph.D."],"dc:creator":["Veach, Matthew"],"dc:date.available":["2026-02-20T08:00:00Z"],"dc:description.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.
"],"dc:identifier":["https://commons.emich.edu/theses/1326"],"dc:subject":["Agentic AI","Ai Governance","Ai Risk Managemnet","Artificial Intelligence","Legal Framework","Microsoft Copilot","Computer Sciences","Other Computer Sciences"],"dc:title":["Agentic AI systems in professional domains: A probabilistic framework for role suitability and legal accountability"],"thesis:degree_discipline":["Information Security and Applied Computing"],"thesis:degree_level":["Open Access Thesis"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T02:17:53Z"}