Automated Incident Intelligence In Supply Chains Using Agentic AI And Root Cause Reasoning

Supply chain operations frequently experience incidents such as delays, shortages, quality failures, and logistics breakdowns. Identifying root causes quickly is critical, yet current incident management processes are largely manual, reactive, and error-prone. Existing systems primarily use rule-based alerts or statistical anomaly detection. Although effective in detecting issues, they lack deep causal reasoning and fail to correlate multi-source data across suppliers, transportation, and operations. This results in delayed resolution and repeated incidents. This paper introduces an automated incident intelligence framework using agentic AI with root cause reasoning. Specialized agents monitor supply chain signals, detect anomalies, and collaboratively perform causal analysis using knowledge graphs and probabilistic reasoning. Generative AI supports hypothesis generation and explanation of root causes in natural language, enabling faster human understanding and response. The proposed system was evaluated on simulated and real operational datasets involving multi-tier supply chains. Results show a 30% reduction in mean time to root cause identification and 22% improvement in incident resolution accuracy compared to traditional approaches. Additionally, the system successfully identified hidden dependencies that were missed by baseline methods. This work demonstrates the effectiveness of agentic AI in transforming incident management from reactive monitoring to proactive intelligence.