Evolvable Multi-Agent Hierarchical Architecture for Contested Logistics Ecosystems
ABSTRACT
Logistics systems that underpin modern supply chains face growing pressure from disruption, adversarial interference, and fragmented data. This paper introduces an evolvable multi-agent hierarchical architecture supported by an AI-Ready Data Intelligence Platform, designed to enable resilient, interoperable, and adaptive logistics operations in contested environments. The architecture organises autonomous digital agents, human decision-makers, and distributed data services across strategic, operational, and tactical layers, supporting coordinated reasoning while preserving local autonomy. A shared data foundation provides semantic alignment, automated quality assurance, adversarial validation, and provenance tracking, enabling agents and human operators to reason explicitly about data trustworthiness, not merely provenance. An illustrative use case grounded in a large-scale logistics modernisation programme demonstrates how the architecture supports sustained decision quality, improved operational resilience, and reduced integration burden under disruption and adversarial pressure. The paper draws lessons applicable beyond defence contexts, contributing architectural guidance for supply chains that must become circular, smart, and adaptive in the face of growing uncertainty and intelligent threat actors.
Keywords: multi-agent systems, contested logistics, supply chain resilience, hierarchical autonomy, AI-ready data platforms, adaptive systems, human-aligned autonomy, adversarial trust
58 June 2026