Automating root cause analysis at scale: Multi-signal correlation for cloud native incident response

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The problem: Humans shouldn’t be correlation engines

At Atlassian’s scale, hundreds of interconnected microservices distributed across multiple regions mean a production incident generates an overwhelming volume of telemetry. The problem is that finding the causal factor in the vast amount of telemetry still relies heavily on human expertise, intuition, and manual cross-referencing.

A typical root cause analysis workflow today looks something like this: an on-call engineer gets paged, opens a metrics dashboard, spots an anomaly in error rate or latency, pivots to a logging tool to search for exceptions within that time window, then opens a tracing UI to inspect individual request paths. They visually correlate patterns across these three separate views, form a mental hypothesis about where the fault lies, and then work backward through the service dependency graph to validate it.

This is a serial, cognitively expensive process. It depends on the responder already knowing which dashboards to check, which log queries to run, and which services are upstream of the one that’s failing. Senior engineers with years of domain knowledge can do this in minutes. Everyone else takes significantly longer, and during a user-impacting incident, every minute matters.

We asked a simple question: what if we automated the hypothesis generation step entirely, so responders could skip straight to validation and resolution?

Our approach: Treat RCA as a multi-signal correlation problem

The insight behind our automated RCA system is that root cause analysis is fundamentally a correlation …

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