Background: Contemporary Logistics Networks are currently under increasing strain due to demand volatility, geopolitical disruption and the increasing challenges of coordinating multi-modal transport operations. Current approaches to managing logistics networks employ disparate information systems and reactive decision-making techniques, which do not suffice to provide the level of resilience and efficiency required by present day commerce.

Objectives: The objective of the research presented in this article is to provide a framework for integrating all aspects involved in operating an Autonomous Multi-Modal Logistics Network. The Cognitive Logistics Network (CLN) framework brings together Agentic Artificial Intelligence (AI), blockchain-based trust mechanisms, Digital Twin simulation and Intelligent Transport Systems (ITS) into a coherent, layered architecture for operating Autonomous Multi-Modal Logistics Systems.

Methods: The research methodology used for developing the conceptual framework consists of systematically reviewing recent research literature related to agentic AI, distributed ledger technology, digital twin modelling, and ITS and then constructing simulated performance scenarios using established parameters for measuring logistics performance in accordance with five dimensions of evaluation (i.e., delivery efficiency, logistics costs, supply chain resilience, fleet utilisation and sustainability indicators).

Results: The model projected a 15-percentage point improvement in on-time delivery rates, a 26% reduction in per-unit logistics costs, a 44% increase in supply chain resilience scores, a 21-percentage point increase in fleet utilization, and a 34% reduction in shipping emissions when using cognitive logistics networks as opposed to conventional systems.

Conclusion: Cognitive Logistics Networks will change how supply chains are managed autonomously. Agentic AI, blockchain, digital twins, and the Internet of Things will work together to create self-reinforcing loops of capabilities that vastly improve operational performance, transparency, and environmental sustainability. Future research will be needed to develop interoperability standards, regulatory governance, and validate at scale in the real world.