From multi-agent logistics to agentic artificial intelligence: review of autonomous coordination in Q-commerce-related last-mile fulfilment

Quick commerce (Q-commerce) relies on fast order fulfilment, high-density last-mile coordination, dynamic routing, and the real-time allocation of couriers, stock, and orders. These characteristics make Q-commerce an appropriate setting for exploring agentic artificial intelligence (AI) and other autonomous coordination technologies, such as multiagent systems, reinforcement learning, and decision support systems. This conceptual and bibliometric review examines how multi-agent systems, reinforcement learning, autonomous decision-making, simulation, auction/trading mechanisms, and bounded large language model (LLM)-assisted decision support have been used to coordinate Q-commerce-related rapid fulfilment and last-mile delivery. A Scopus search of titles, abstracts, and author keywords initially identified 29 records. After title, abstract, and full-text screening, 16 eligible studies were retained and analysed through keyword co-occurrence analysis, overlay visualization, and thematic synthesis. The findings reveal that existing research is predominantly oriented toward technical and operational topics, including multi-agent reinforcement learning, delivery optimization, courier coordination, delivery-zone partitioning, platform-based allocation, and sustainable last-mile delivery. However, sociotechnical concerns remain underexplored, including human autonomy, accountability, transparency, privacy, fairness, safety, and platform governance. The review shows that the evidence base is dominated by multi-agent and autonomous logistics coordination rather than by mature deployments of agentic AI in Q-commerce. It therefore positions agentic AI as an emerging conceptual extension of autonomous logistics coordination that requires further sociotechnical, governance, and empirical validation