Agentic AI for Supply Chain Resilience
A Mixed-Method Study of Agentic AI Design Principles Informed by Avionics Practices
A. Pogna (TU Delft - Civil Engineering & Geosciences)
Patrick Stokkink – Mentor (TU Delft - Technology, Policy and Management)
Arjan van Binsbergen – Graduation committee member (TU Delft - Civil Engineering & Geosciences)
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Abstract
Global supply chains increasingly experience disruptions due to climate events, geopolitical changes, and cyber threats. Artificial intelligence (AI) presents opportunities to enhance supply chain resilience; however, these systems may introduce additional risks if not implemented with adequate safeguards. This study examines strategies for designing and governing AI systems to strengthen supply chain resilience while minimizing unnecessary complexity. A mixed-methods approach is employed, combining interviews with AI and supply chain experts and two focus groups. The research identifies three agent design archetypes that clarify the practical contributions of AI to supply chain resilience: Diagnostic and Monitoring Agents, which provide early warnings for slow-moving risks; Response and Coordination Agents, which accelerate disruption recovery by synthesizing fragmented data and take limited actions; and Interface and Learning Agents, which disseminate tacit knowledge and reinforce feedback loops. Furthermore, a two-dimensional evaluation framework is introduced. The first dimension (R-Dimension) as- sesses whether a proposed AI concept credibly enhances supply chain resilience, while the second dimension (D-Dimension) determines the appropriate level of autonomy and implementation feasibility. The Dimensions Interaction Matrix maps resilience value against governability across six decision zones, guiding practitioners from concept rejection to bounded autonomy. The framework also maps Generative AI (GenAI) and Agentic AI to specific Levels of Automation (LoA), enabling practitioners to justify whether a deterministic approach, GenAI, or Agentic AI is suitable for each supply chain decision point. The findings indicate that effective deployment of Agentic AI requires investment in disruption readiness through synthetic scenario testing and organizational preparedness with defined escalation protocols and human override authority. This research provides practical frameworks for practitioners and identifies areas for future validation with real-world data.