Modern observability platforms excel at collecting telemetry but leave interpretation to human engineers, creating a bottleneck as cloud-native systems scale. The proposed solution is an AI architectural layer sitting between telemetry collection and human decision-making — preprocessing raw signals through noise filtering, anomaly detection, and statistical analysis before feeding structured context to an LLM that generates natural-language explanations. This approach enables automatic cross-signal correlation across metrics, logs, and traces, and shifts the interaction model from dashboard exploration to conversational querying. Red Hat's OpenShift AI observability summarizer is presented as an implementation of this pattern, aimed at reducing operational toil for SRE teams managing complex multi-cluster and AI workload environments.
