CirrusBench: Evaluating LLM-based Agents Beyond Correctness in Real-World Cloud Service Environments

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• Existing benchmarks for LLM-based agents largely rely on synthetic environments that fail to capture the diversity and unpredictability of authentic customer inputs.limitation 95%
• CirrusBench is a novel evaluation framework distinguished by its foundation in real-world data from authentic cloud service tickets.method 95%
• CirrusBench introduces novel Customer-Centric metrics to define agent success, quantifying service quality through metrics such as the Normalized Efficiency Index and Multi-Turn Latency to explicitly measure resolution efficiency.method 90%
• State-of-the-art LLM-based agents frequently struggle in complex, realistic multi-turn tasks.result 85%
• State-of-the-art LLM-based agents fail to meet the high-efficiency standards required for customer service.result 85%
• Explicit thinking capability in LLMs is not always beneficial for success rates and efficiency metrics in cloud service tasks.result 80%
• The thinking model DeepSeek-R1 achieves a lower Pass@1 Success Rate (7.9%) in tool-use scenarios than its non-thinking counterpart, DeepSeek-V3.2 (9.6%).result 95%
• The CirrusBench framework preserves the intricate multi-turn logical chains and realistic tool dependencies inherent to technical service environments.method 90%

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