Confluent's Streaming Agents leverage built-in anomaly detection capabilities in Apache Flink to automatically identify and respond to unexpected deviations in data streams, offering a proactive approach to data operations. The system uses machine learning functions like ml_forecast() and ml_detect_anomalies(), which employ the ARIMA model for time-series analysis and real-time anomaly detection, to improve data quality and operational responsiveness across various industries such as financial services, retail, IoT, and SaaS. This approach allows agents to act on high-quality anomaly signals rather than raw events, facilitating prompt decision-making and reducing false positives common with traditional static threshold monitoring. The solution is available on Confluent Cloud, providing an out-of-the-box, cost-effective deployment without requiring extensive machine learning expertise, and sets the stage for future enhancements to handle complex, multivariate anomalies.