Large-scale cloud systems underpin modern computing, hosting diverse components to deliver critical services worldwide. A single fault—such as an outage or misconfiguration—can simultaneously disrupt thousands of users. Such large-scale faults, referred to as batch failures, are characterized by many affected instances across the same subject within a short time window, typically stemming from a shared root cause. Handling these failures efficiently requires anomaly localization, but existing approaches offer insufficient support to engineers, making the process time-consuming and cognitively demanding. To address this, we propose \textbf{Aloha}, a human-in-the-loop agent framework for anomaly localization based on contrast analysis. Aloha operationalizes the entire batch failure handling pipeline, providing scenario- and data-aware guidance along with interpretable root-cause patterns for engineers. Pilots on real-world batch failure cases in Microsoft’s cloud show that Aloha streamlines data handling, supports contrast-based anomaly localization, and makes the process more practical and accessible, offering a promising step toward human-centered, scalable failure management in large-scale cloud systems.