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Agentic Control Towers: Multi Agent LLM RL Orchestration for Autonomous and Resilient Supply Chains
Abstract
Supply chains exist today under chronic volatility, uncertainty, complexity, and ambiguity. Batch based planning and decision rights fragmentation render companies sluggish to detect disruption and costly to rebound. This paper develops an integrated concept—Agentic Control Towers (ACTs)which combines large language model (LLM) agents, multi agent reinforcement learning (MARL), and digital supply chain twins to provide closed loop, explainable autonomy for plan, source, make, move, and serve. We integrate findings across inventory and logistics reinforcement learning, LLM tooling and multiagent coordination, digital twin orchestration, and resilient operations. We position ACTs as a decision fabric that continuously senses, simulates, negotiates, and performs while placing guardrails for safety, sustainability, cost, and service. The paper provides an integrated architecture, end to end decision flows, stability and governance design decisions, and evidence informed pathways of impact. We also establish evaluation criteria.
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