AUTONOMOUS WORKFLOW OPTIMIZATION USING MULTI AGENT AI SYSTEMS AI AGENTS MANAGE STATIONS, WIP, AND TASK HANDOFFS

Abstract

Industrial operations have adopted artificial intelligence because it enables them to create automated production systems that continuously enhance their efficiency through multi-agent AI systems. The existing methods for workflow management depend on unchanging scheduling systems and human operators, which cannot handle the unpredictable and changing conditions that businesses encounter in their actual operations. The research presents and evaluates a system design, which uses autonomous AI agents to oversee workstation operation, WIP inventory control, and task transfer management throughout complex manufacturing and service operations. The proposed system, which uses reinforcement learning and game-theoretic negotiation and distributed consensus mechanisms, achieves better results in throughput and cycle time and resource utilization than standard methods. The results from experimental simulations and real-world deployment case studies show that multi-agent coordination decreases average cycle time by 34% and WIP accumulation by 27% while increasing station utilization to more than 91%. The paper explores three aspects, which include emergent coordination behaviors and failure resilience and the ethical implications that arise from using autonomous agents for decision-making in essential workflow operations.