Observability for AI-Enabled Cloud-Native Networks: A Unified Framework Integrating OpenTelemetry, CortexDB, Loki, GenAI, and RAG

Abstract

Modern cloud-native networks built on ephemeral microservices generate massive volumes of telemetry data across disparate sources, making comprehensive observability increasingly challenging. This article presents a unified framework that integrates OpenTelemetry, specialized time-series databases, and advanced artificial intelligence techniques to address these challenges. The framework establishes a robust telemetry foundation by leveraging OpenTelemetry for standardized data collection while elevating events to first-class citizenship alongside traditional metrics, logs, and traces. This foundation is anchored by scalable storage solutions CortexDB for time-series metrics and Loki for log aggregation providing cost-effective persistence for historical analysis. The framework transforms raw telemetry into actionable intelligence through Generative AI, which automatically analyzes multi-modal observability data, identifies significant patterns, and proactively flags anomalies. To enhance contextual awareness, Retrieval-Augmented Generation (RAG) incorporates historical operational data and domain knowledge, significantly improving the accuracy and relevance of generated insights. The intelligent alerting system transcends traditional threshold-based approaches by implementing pattern-based, predictive, and contextual alerting through an AI-enhanced alert manager. Automated response capabilities range from diagnostic data gathering to fully autonomous remediation for well-understood issues. The integrated article dramatically reduces mean time to detection and resolution, decreases false positives, improves proactive issue identification, and enables significantly more efficient resource utilization, ultimately transforming observability from a reactive troubleshooting aid to a proactive operational intelligence platform for cloud-native networks.