Authors - Amna Ali, Rida Hijab Basit Abstract - With the advent of agentic Artificial Intelligence, systems have demonstrated significant ability to understand data and respond to changing business environments without human assistance. Agentic AI is largely being used in supply chain management (SCM) systems for automating the supply chain tasks - demand forecasting and planning, logistics and transportation optimization, supplier management and risk reduction, and warehouse management. Use of agentic AI in SCM represents a drastic shift from traditional rule-based systems to automated goal-driven systems that operate without human intervention. Such systems are supported by Natural Language Processing and deep learning models which have made the supply chain processes much easier, efficient and less prone to error. The organizations that have incorporated agentic AI in their business processes have reported operational efficiency and cost effectiveness. However, such advancements in technology have raised concerns related to privacy ethics and data security. In this paper, we have conducted the systematic review of the existing research on the usage of Agentic AI in Supply Chain Management. The paper discusses characteristics of agents in SCM, different types of architectures and analyses the limitations and challenges related to the usage of AI agents in supply chain management.
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