Automated Root Cause Analysis: The Complete Guide
Automated root cause analysis, end to end: how to automate the hours of investigation before the fix, on the telemetry tools you already run. Get the guide.
The fix for most incidents takes minutes: a rollback, a config change. The hours go to finding out what to change. Automated root cause analysis hands that search to software, and gives your engineer a causal chain to check with the evidence attached.
Published results show how wide the spread is. Microsoft reports 76.6% accuracy from a system grounded in past incidents. On an open benchmark, the best model working from raw telemetry solved 11% of cases. Both used frontier models. The guide shows what made the difference, and how to build for it.
It is written for the people who will build it: working configuration and code on open-source tools, a worked incident from alert to approved rollback, and nothing you need to buy to follow along.
Inside the guide
The eight techniques behind automated RCA, from rules to AI agents, compared in one table.
A nine-step build on OpenTelemetry, Prometheus, Loki and Tempo, with configuration and code.
How to put an AI agent on top: tool design, Context Engineering, and six rules that stop a confident wrong answer.
A worked incident, from the alert to an approved rollback, with every piece of evidence shown.
How to measure it: a replay suite of past incidents, and the metrics that move before MTTR does.
Twelve failure modes, what each looks like in production, and the fix.
A five-level maturity model and a 90-day plan.
Where NeuBird fits alongside the observability tools you already run.
FAQ
Frequently asked questions
What is automated root cause analysis?
Automated root cause analysis hands the investigation to software: the search, during an incident, for what to change. The output is a causal chain with the supporting evidence attached, for an engineer to review. The fix itself, a rollback or a config change, usually takes minutes. The hours go to the search, and that is the part automated RCA takes on.
How accurate is automated root cause analysis?
Accuracy depends on how the system is grounded. Microsoft reports 76.6% accuracy from a system grounded in past incidents. On an open benchmark, the best model working from raw telemetry alone solved 11% of cases. Both used frontier models, so the grounding in context and past incidents is what made the difference.
What telemetry tools do you need to build automated RCA?
The guide builds on open-source tools: OpenTelemetry, Prometheus, Loki, and Tempo. The configuration and code are included, and there is nothing you need to buy to follow along.
Can an AI agent do root cause analysis reliably?
It can, when the agent is built for it: sound tool design, Context Engineering, and rules that stop a confident wrong answer. Reliability is measured against a replay suite of past incidents, and the metrics that move before MTTR does tell you whether the agent is working.
How does NeuBird fit with the observability tools we already run?
NeuBird's Production Ops Agent runs on the Agentic Reliability Center alongside the observability tools you already run. It queries your telemetry in place, with no bulk telemetry retention, and analyzes 15+ sources in parallel to isolate root cause in under 5 minutes at 94% accuracy. It then stages a proposed fix and waits for human approval.
Related Resources
See NeuBird in action
Root cause in minutes, not war rooms.