Cloud-OpsBench: A Reproducible Benchmark for Agentic Root Cause Analysis in Cloud Systems

A research team from institutions including The Chinese University of Hong Kong has published Cloud-OpsBench, a groundbreaking benchmark designed to evaluate and train AI agents for Root Cause Analysis (RCA) in complex cloud environments. The core innovation is its 'State Snapshot Paradigm,' which constructs a deterministic digital twin of a cloud system, reconciling the long-standing tension between ecological validity and reproducibility in AI operations research. This allows for the systematic testing of agentic reasoning—where AI actively investigates problems—rather than just passive classification, across a massive dataset of 452 distinct fault cases spanning 40 different root cause types within a simulated Kubernetes stack.

The benchmark is engineered as a multi-purpose research infrastructure with three key functions. First, as a Data Engine, it automatically harvests high-quality reasoning trajectories from successful agent interactions, creating datasets to bootstrap Supervised Fine-Tuning (SFT) for more efficient Small Language Models. Second, it acts as a Reinforcement Learning environment, transforming the high-risk, high-stakes task of live cloud debugging into a safe, low-latency sandbox where agents can be trained via policy optimization without causing real outages. Finally, as a Diagnostic Standard, its process-centric evaluation protocol helps uncover architectural bottlenecks in multi-agent RCA systems, guiding the design of more robust and specialized operational AI. This work represents a significant step toward autonomous cloud management, providing the essential tools to move beyond simple alerting to proactive, reasoning-based system repair.