THE EVOLUTION OF AGENCY: THE TRANSFORMATION OF AI TOOLS IN SUPPLY CHAIN MANAGEMENT

Abstract

This study examines the evolution of artificial intelligence (AI) in supply chain management through the transition from instrumental functionality to agent-based subjectivity. It is argued that the dominant scientific discourse remains largely focused on digitalization and process automation, whereas real-world applications of AI demonstrate a significantly deeper transformation associated with the progressive delegation of decision-making authority to algorithmic systems. The paper identifies and systematizes the main types of AI applied in supply chains, including analytical, predictive, generative, and agent-based models, and substantiates their complementary roles within an integrated digital management architecture.

The main scientific contribution lies in developing an original approach to identifying the stages of the evolution of AI subjectivity, encompassing instrumental, analytical-recommendation, executive-adaptive, and agent-autonomous levels. In addition, the study systematizes the key characteristics of AI agents, including autonomy, proactivity, adaptability, learning capability, and multi-agent interaction, which determine their role as active participants in management processes. It is demonstrated that modern AI agents are capable not only of data processing and predictive analytics but also of initiating, executing, and coordinating managerial actions in real time. Particular attention is given to Retrieval-Augmented Generation (RAG) as a critical mechanism for enhancing decision reliability and mitigating hallucination risks in agent-based systems. Based on the analysis of practical cases, the study demonstrates that the integration of agentic AI significantly improves the efficiency, adaptability, and resilience of supply chains. It is concluded that the further evolution of logistics systems will be associated with the development of multi-agent ecosystems capable of decentralized coordination and effective functioning under conditions of high uncertainty.


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