Supply chain disruptions cost global manufacturers an average of $184 million annually per event, with traditional response systems requiring 3-5 days for decision-making during critical disruption windows. This paper addresses the operational challenge of reducing disruption response time and cost through autonomous multi-agent AI systems. We develop and test an agentic AI framework that autonomously detects, analyses, and responds to supply chain disruptions across procurement, logistics, and inventory management. Using real disruption data from 127 manufacturing facilities over 18 months, we compare our autonomous agent system against three baseline approaches: manual operations management, rule-based automation, and predictive analytics. Results demonstrate 87% reduction in response time (from 72 hours to 9.4 hours), 34% decrease in disruption costs ($62.5M to $41.3M annually), and 91% improvement in forecast accuracy (MAPE reduced from 23.4% to 2.1%). The autonomous system achieved R² of 0.94 for demand prediction versus 0.67 for traditional methods, with ROI of 312% realized within 14 months of deployment. This research provides operations managers with quantified performance benchmarks and implementation guidelines for autonomous disruption response systems.