Evolvable Multi-Agent Hierarchical Architecture for Contested Logistics Ecosystems
• Tactical layer: Agents focus on near-real-time execution, sensing, and response. They operate within boundaries set by higher layers, adapting local decisions rapidly to immediate conditions without requiring centralised approval for routine actions.
Figure
5-2: Agent layer hierarchy showing representative agent roles and information flows.
Coordination is achieved through goal decomposition, constraint propagation, and feedback mechanisms( Figure 5-2). Strategic objectives decompose into operational goals and executable tactical tasks. Feedback flows upward, allowing higher-level agents and humans to assess state and adjust intent. Agents operate under partial observability, enabling continued functioning even when connectivity is degraded.
5.2 EVOLVABILITY AND HIERARCHICAL AUTONOMY
By delineating responsibilities across layers, local agents adapt to immediate conditions while remaining aligned with higher-level intent. Tactical agents may adjust routing in response to realtime disruptions; operational agents assess broader resource impacts; strategic agents revise objectives based on emerging risks. This separation reduces coupling, facilitating adaptation without wholesale redesign. [ 4 ]
The architecture supports human-aligned autonomy throughout. Rather than seeking full automation, it recognises the continued importance of human judgement in high-consequence decisions. Human decision-makers are first-class participants, supported by AI-enabled analytics while retaining authority over critical choices. [ 12 ]
5.3 THE AI-READY DATA INTELLIGENCE PLATFORM
The effectiveness of any multi-agent architecture depends on the quality, trustworthiness, and integrity of underlying data. In contested environments, data may be heterogeneous, intermittent, variable in quality, or subject to intentional manipulation. The architecture is underpinned by an AI-Ready Data Intelligence Platform serving as a shared data foundation( Figure 5-3).
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