How Mezmo Uses Active Telemetry for Faster AI Root Cause Analysis

AI-powered root cause analysis only works when the data going into the model is clean, relevant, and structured. In this demo, we show how Mezmo's Active Telemetry approach helps engineers and SREs move from noisy application errors to immediate clarity.

Using a restaurant ordering application running in Kubernetes, we trigger a database connection pool exhaustion issue and walk through two ways to investigate it with Mezmo:

First, the Mezmo AI Assistant analyzes telemetry patterns and identifies the likely root cause with recommendations for remediation. Then, an external agent connects through Mezmo's MCP server, uses RAG over documentation, and incorporates Kubernetes context to produce a deeper timeline, findings summary, RCA, and prevention guidance.

The key difference: Mezmo is not simply putting an LLM on top of logs. It uses deterministic pre-processing to curate messy telemetry into structured signal before the model sees it, helping make RCA more consistent, explainable, and fast.

Built for platform engineers and SREs who need immediate clarity during incidents — not another tool that drowns the model in noise.

See Mezmo Active Telemetry in action → https://www.mezmo.com/platform/active-telemetry

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