Kubernetes environments generate enormous telemetry volumes, yet most AI observability integrations treat logs and traces as raw text — producing hallucination-prone, unreliable incident responses.

This session presents a production-validated four-layer architecture built on CNCF-native tooling: OpenTelemetry for signal collection, a Model Context Protocol (MCP) server as a typed semantic query layer, and AI-driven agents for proactive root-cause analysis. The speaker shares production lessons from running this architecture at petabyte scale across multi-region Kubernetes deployments at Workday.

Attendees will understand why raw OTel pipelines fail with LLMs, how an MCP server exposes telemetry as structured, queryable context, and the cardinality and latency tradeoffs encountered in production. The session closes with a code walkthrough of a lightweight MCP server integrating with Prometheus or Mimir, and practical criteria for when agentic observability genuinely reduces MTTR.