Agentic AI for IT and Beyond: A Qualitative Analysis of Capabilities, Challenges, and Governance

Abstract

Agentic AI represents a leap forward in AI, characterized by autonomous decision-making, adaptive reasoning, and innovative collaboration in dynamic environments. In their shift away from mere automation towards reflective, goal-oriented behavior, these promises are significant: in IT operations, real-time analytics, strategic decision-making, and more. Nevertheless, and notwithstanding its increasing importance in industry, there is no coherent framework within the academic literature that captures the technological, ethical, and governance aspects of Agentic AI. This study employs a qualitative approach, incorporating thematic analysis and comparative case studies, to interpret the results from academic sources, industrial documents, and regulatory publications from 2023 and 2024. The paper integrates technical with interdisciplinary literature and considers four key areas: (1) the functional architecture and mechanisms of Agentic AI, (2) operational value via AIOps platforms including Moogsoft and Dyna-trace, (3) evolving risks such as bias, data abuse, and autonomy misalignment, and (4) regulatory and ethical lacunae in existing oversight statues. Further, the work uncovers recurring themes, such as explainability, human-AI collaboration, and fairness that are essential for the design and deployment of these systems in the future. The work surfaces recurring themes, like explainability, human-AI partnership, and fairness that are crucial to the way in which these systems are designed and used in the future. We are still at the phase of approximate common knowledge in AI. To solve this and other pressing matters, the paper argues for using a point of view, called Agentic AI in-the-making-novel methodology that centers on an "eye-on-eye" interaction between human and AI agencies. By merging theoretical models with practical instances, the paper establishes a holistic frame for deploying and constraining potential Agentic AI. It also provides an initial slate of recommendations to policymakers, innovators, and industry leaders on how to encourage responsible innovation that focuses on transparency, accountability, and interdisciplinary collaboration in the development of new intelligent systems.