A Multi-Dataset Benchmark for Evaluating LLM Agents in Microservice Failure Diagnosis
LLM-based agents are reshaping microservice operations into AgentOps, where benchmarks are key to evaluating failure diagnosis over multimodal observability data. However, existing benchmarks remain largely outcome-oriented: they score only the final answer and fail to assess the systematic reasoning process in failure diagnosis. We address this gap by introducing two large-scale datasets (AIOps2025 and RCA100) under a reasoning-process evaluation paradigm that assesses agentic diagnostic capability along three dimensions: Localization (where the fault occurs), Identification (what type of fault it is), and Reason (whether the reasoning trace is grounded in relevant evidence). Together, t...
| Authors | Yuanhong Cai, Xiaohui Nie, Kanglin Yin et al. |
|---|---|
| Primary category | cs.SE |
| Published | 28 June 2026 |
| Last updated at source | 28 June 2026 |
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