Real Time API Anomaly Detection and Predictive Alerts

Modern software systems, built on distributed API and microservice architectures, generate vast volumes of telemetry data that make traditional monitoring ineffective. This complexity leads to “alert fatigue” and prolonged downtime, as IT teams struggle to manually correlate events and find the root cause of failures. This paper introduces a novel, three-stage AIOps (Artificial Intelligence for IT Operations) platform designed to provide a complete, automated observability loop for API infrastructures. The objective of this platform is to unify failure management by integrating (1) a real-time, ensemble-based anomaly detection engine using time-series forecasting and Graph Neural Networks (GNNs), (2) a predictive alerting system powered by Explainable AI (XAI) to diagnose “why” an anomaly occurred, and (3) a Generative AI-driven remediation module that performs automated root cause analysis and suggests fixes. This integrated approach moves beyond simple detection, providing actionable, explained insights that break the cycle of recurring failures, reduce Mean Time to Resolution (MTTR), and enhance system reliability.