This article discusses the importance of real-time anomaly detection in multi-agent AI systems. Such systems are increasingly complex and interconnected, making them more prone to unexpected anomalies that can lead to catastrophic failures. The article explores five types of anomalies in multi-agent systems: behavioral, communication, resource utilization, performance, and emergent behavioral anomalies. It also discusses practical methods for detecting these anomalies in real-time, including statistical baseline monitoring, machine learning model deployment, agent interaction graphs, multi-level alert systems, and automated response workflows. The article concludes by emphasizing the need for sophisticated tooling to secure multi-agent AI systems against emerging threats and vulnerabilities.