CLASSIFICATION OF AUTONOMY LEVELS OF AGENTIC AI IN IT PROJECT MANAGEMENT
Abstract
The rapid integration of agentic artificial intelligence into project management practice has outpaced the conceptual instruments available for governing such systems. Organizations adopt autonomous AI agents without a structured framework for determining the appropriate degree of delegation across diverse project tasks. The academic literature, in turn, has not yet produced a classification of AI autonomy levels adapted to the specifics of project management. The relevance of the topic stems from the convergence of three trends: documented risks of failure for agentic initiatives, persistent gaps in managerial preparedness for AI-related work, and the projected redistribution of tasks between human professionals and autonomous systems. The aim of the study is to develop a five-level autonomy scale tailored to IT project management, where stakeholder multiplicity, process group heterogeneity, governance requirements, and ethical accountability shape the conditions for AI integration. The methodological foundation rests on a systematic review of peer-reviewed literature published between 2019 and 2026 and indexed in the Scopus, Web of Science, and Google Scholar databases. The review is complemented by the analysis of industry and analytical reports issued by leading professional and research organizations. A comparative assessment of five autonomy frameworks originating in adjacent disciplines (general automation theory, autonomous driving, human-computer interaction, artificial general intelligence, and software engineering) is performed against six criteria reflecting the requirements of the project management context. The proposed scale spans five levels, from the absence of AI involvement (A0) through assistive (A1), analytical (A2), and partner (A3) configurations to AI in a managerial role (A4). Each level is characterized by four parameters: delegated task type, control mechanism, responsibility distribution, and project manager competency requirements. Operation outside the defined operational design domain is deliberately excluded on ethical, technological, and regulatory grounds. Governance demands across transitions exhibit nonlinearity: progression up to the analytical level remains predominantly technical, advancement to the partner level becomes organizational, and the shift to the managerial role acquires strategic significance. The practical value of the scale lies in its applicability as a reference instrument for IT organizations. It supports the design of strategies for autonomous AI agent integration, the formalization of governance protocols, and the planning of professional development programs that address the evolving role of the project manager in the era of agentic AI.
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