The Impact of Agentic AI Technology on the Efficiency of IT Personnel Assessment in the Intelligent Information Systems

Abstract

The study aimed to assess the impact of integrating Agentic artificial intelligence (AI) on the effectiveness of human resource (HR) management, particularly in evaluating information technology (IT) specialists and developing individual development plans within intelligent information systems. Theoretical modelling was combined with applied scenario analysis, within which four companies in Ukraine, Poland, and Germany operated a traditional assessment chain and an automated chain in parallel, incorporating neural networks, large language models (LLMs), generative AI, machine learning, deep learning, vector databases, and structured data frames. As a result, the average time to form a competency profile for a single developer was reduced from 18.4 hours to 10.6 hours, i.e., by 42%; the accuracy of job suitability predictions increased from 71.3% to 89.2%; in 32% of cases, the Agentic approach detected the risk of staff turnover, while the traditional procedure did not demonstrate statistically significant results; indirect departmental productivity, measured by the number of completed project tasks, increased by 14.5%; and the average duration of new employee integration was reduced by 3.2 working days. The share of subjective assessments by managers decreased by 41%. Assessment costs were reduced by 18.9% without significant changes to the infrastructure. The results show that the combination of Agentic AI, contextual generation mechanisms, and machine learning represents a viable business automation tool capable of increasing the speed of personnel decision-making, optimising individual development trajectories, and reducing the financial and time costs of HR management. The practical significance of the study is determined by the fact that its results can be used by IT company managers and HR analysts to improve the accuracy of developer assessments, develop individual development plans, and reduce the costs of HR analytics.