Evolvable Multi-Agent Hierarchical Architecture for Contested Logistics Ecosystems
The proposed evolvable multi-agent hierarchical architecture, underpinned by an AI-Ready Data Intelligence Platform, provides such a blueprint. It organises autonomous agents, human decision-makers, and distributed data services into a coherent ecosystem capable of coordinated reasoning across strategic, operational, and tactical timescales. By explicitly addressing data heterogeneity, uncertainty, and adversarial manipulation, and by embedding zero-trust verification principles throughout [ 14 ], the architecture supports graceful degradation and sustained decision quality in contested environments.
Through an illustrative logistics modernisation scenario, the paper demonstrated capability improvements: improved resilience, faster decision cycles, reduced integration burden, enhanced interoperability, and adversarial detection. These are achieved through architectural choices prioritising evolvability, data trust, and human-aligned autonomy.
The implications extend beyond the originating context. As supply chains become more circular, smart, and adaptive, this work highlights the importance of treating data trust, including its adversarial dimension, as a first-class concern, embedding autonomy within clear hierarchies of responsibility, and designing for disruption and deception as normal conditions. By focusing on architecture rather than point solutions, it provides a foundation for sustained innovation in logistics systems operating in increasingly contested environments.
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