Topology-Aware AI Agents for Observability: Automating SLO Breach Root Cause Analysis

Topology-Aware AI Agents for Observability: Automating SLO Breach Root Cause Analysis Modern cloud systems are complex distributed architectures where a single user journey may depend on dozens of services running across multiple infrastructure layers. When a Service Level Objective (SLO) breach occurs, identifying the root cause often requires navigating logs, metrics, traces, service dependencies, and infrastructure relationships. In many organizations, this investigation is still manual and time-consuming . In a recent project, I explored how AI agents can automate incident investigation by combining: Observability data Service topology Kubernetes infrastructure context Historical incident knowledge Graph-based reasoning This approach reduced investigation time from 20–30 minutes to under a minute for certain SLO breaches. This article introduces the concept of Topology-Aware AI Agents and how such a system can be implemented using AWS services and graph-based system modeling. The P