This study develops and validates an integrated agentic-intent AI framework for strengthening supply chain resilience in Nigeria’s petroleum logistics and axle-load enforcement systems. Drawing on dynamic capabilities, supply chain resilience, and cyber-physical systems theories, the framework positions intent AI as a predictive sensing layer and agentic AI as an autonomous execution layer, with resilience reflected through robustness, agility, and adaptability. A hybrid computational design integrates PLS-SEM, agent-based modelling (ABM), and system dynamics (SD) to examine causal relationships, behavioural interactions, and system-level feedback. Stochastic simulation generates trip-level observations reflecting operational pressure, infrastructure stress, security risk, and regulatory conditions. Results indicate that agentic AI exerts a stronger direct effect on resilience than intent AI, while intent AI significantly strengthens agentic AI capability. Agentic AI also partially mediates the intent AI-resilience relationship. Infrastructure quality and regulatory strength enhance AI effectiveness, whereas security risk and fuel-price volatility constrain resilience. The study advances a predictive-to-autonomous pathway for intelligent, adaptive logistics resilience in emerging economies.
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