AI-Driven Benchmark and Evaluation Framework for Incident Diagnosis in Cloud-Scale Systems

The exponential growth of microservice architectures and cloud-native deployments has fundamentally transformed the landscape of incident management. As organizations operate platforms encompassing thousands of interdependent services, the challenge of rapid, accurate incident diagnosis has become a critical engineering problem. This paper presents the AI-Driven Diagnostic Benchmark Framework (AI-DBF), a comprehensive evaluation system designed to rigorously assess AI-based incident diagnosis tools across cloud-scale environments. AI-DBF introduces a curated corpus of 4,812 labeled production incidents spanning six canonical failure categories, a reproducible fault-injection harness targeting Kubernetes-based deployments, and a multi-dimensional scoring protocol evaluating detection accuracy, diagnosis latency, root-cause attribution, and graceful degradation under telemetry noise. Experimental evaluation across four representative systems demonstrates that AI-DBF exposes meaningful performance differentials invisible to existing ad hoc assessments. Our framework achieves a mean-time-to-detect (MTTD) of 6.2 minutes, an F1 score of 0.941 for incident classification, and sustains sub-10-second P95 diagnosis latency at 5,000-service scale. AI-DBF is designed to be vendor-neutral, reproducible, and continuously extensible, providing the research community with a shared foundation for fair, evidence-based comparison of emerging diagnostic AI systems.