KubeVision AI: Kubernetes-Native Observability and Root Cause Analysis Platform

Abstract: Managing containerized workloads on Kubernetes simplifies deployment and horizontal scaling, but diagnosing failures in production remains challenging. When a service experiences an issue, errors often propagate across dependent components, triggering waves of correlated alerts that conceal the underlying fault. Conventional Site Reliability Engineering (SRE) workflows depend on bytecode instrumentation agents or sidecar proxies, which introduce runtime latency and memory overhead. At the same time, automated Root Cause Analysis (RCA) techniques based on static thresholds or causal dependency graphs struggle under high-dimensional metric noise and bursty traffic patterns. While Large Language Models (LLMs) offer strong contextual reasoning, single-prompt implementations suffer from hallucinations, context window exhaustion, and alert flooding.
This survey paper reviews current methodologies across cloud-native observability, anomaly detection, and automated root cause analysis. We evaluate telemetry collection mechanisms (bytecode injection, sidecars, and kernel-level eBPF), time-series anomaly detection pipelines, causal dependency discovery, and emerging LLM-based agentic frameworks. We benchmark leading industry platforms (Dynatrace, Datadog, Pixie, K8sGPT) alongside academic systems (CauseInfer, MicroCause, CloudRanger, RCAAgent, TriFleetRCA). To address key research gaps around instrumentation overhead, alert storms, and ungrounded LLM outputs, we propose the architectural design of KubeVision AI: a Kubernetes-native framework combining kernel-level eBPF telemetry, a two-stage anomaly filter (Isolation Forest paired with an LSTM Autoencoder), an alert deduplication gateway, and a multi-agent LangGraph diagnostic pipeline with programmatic reflection gates. Finally, we discuss open research challenges and position time- series capacity forecasting as a planned extension.

Keywords: Kubernetes Observability, Extended Berkeley Packet Filter (eBPF), Root Cause Analysis (RCA), Multi- Agent Systems, Large Language Models (LLMs), Anomaly Detection, AIOps.