The rapid growth in scale and complexity of modern software systems has intensified the need for intelligent and reliable IT operations. While Artificial Intelligence for IT Operations (AIOps) addresses some challenges, existing solutions predominantly rely on isolated, task-specific models that struggle with interpreting multimodal data, incur high maintenance costs, and lack sufficient transparency. Operations Large Language Models (OpsLLMs) offer unified, knowledge-rich reasoning capabilities, yet their evaluation faces significant barriers, including the absence of Ops-centric evaluation taxonomies, limited availability of public datasets, simplistic question-generation methods, and inadequate quality standards for comprehensive operations tasks.

We present Eagle, a comprehensive benchmarking framework tailored for evaluating OpsLLMs. Deployed inside Huawei, Eagle ingests enterprise product documentation and synthesizes 4{,}845 domain-grounded QA pairs across logs, metrics, traces, and configurations, which are paired with a standardized model evaluation system. This deployment supported multiple evaluations of OpsLLM and culminated in an internal horizontal benchmarking report that informed model selection and rollout decisions. Methodologically, Eagle (i) defines an operations-centric taxonomy aligning core LLM abilities with end-to-end operations tasks; (ii) implements an automated question-generation pipeline with multi-granular quality controls validated by human annotation; and (iii) provides reproducible evaluation suites and metrics for scenario-driven reasoning. In offline studies, Eagle-generated test suites improve expert-rated rubric scores by 22%–49% over state-of-the-art baselines, enabling more precise assessments of anomaly detection, fault diagnosis, and root-cause analysis abilities in OpsLLMs. To foster community adoption and reproducibility, we open-source the framework (https://github.com/NickLennonLiu/eagle_code/) and a sanitized dataset (https://github.com/NickLennonLiu/eagle_data/). By bridging general LLM evaluation and operations practice, Eagle delivers a deployable foundation for advancing large-model applications in AIOps.