Relvy AI: Automated On-Call Runbooks for Engineering Teams!

Engineering Autonomous Root Cause Analysis: Beyond LLM Heuristics The challenge of automating on-call response is fundamentally a problem of signal-to-noise ratio and verifiable execution. While Large Language Models (LLMs) have demonstrated exceptional capabilities in code generation and textual reasoning, they struggle significantly with the "OpenRCA" problem—performing root cause analysis (RCA) on live telemetry data. The primary failure mode for naive AI integrations is the "hallucinatory path," where an agent attempts to infer causality from sparse or noisy metrics without a bounded problem space. At Relvy, we have architected a system that shifts the paradigm from generative "problem solving" to deterministic, runbook-oriented execution. This article explores the engineering requirements for building a reliable, autonomous on-call agent that avoids the pitfalls of generic LLM agents. The Problem: Why Generative RCA Fails Current benchmarks indicate that even high-parameter models