CTBench: Evaluating Troubleshooting Capabilities of AI Agents in Realistic Telecom Network Operations

Abstract

While autonomous agents show promise for network operations, existing telecom troubleshooting evaluations fail to capture the partial observability and heterogeneity of real-world telecom environments. We introduce CTBench, a public benchmark evaluating agent troubleshooting capabilities in root cause analysis (RCA) and path restoration. Constructed by domain experts, CTBench features rich metadata and golden evidence trajectories, utilizing metrics that assess both final accuracy and diagnostic reasoning. Our evaluation of state-of-the-art agents reveals that while they excel at path endpoint identification, they severely underperform in RCA. Crucially, even when agents produce correct answers, they frequently fail to gather the requisite diagnostic evidence demanded in operational practice. Finally, we demonstrate that increased computational expenditure does not guarantee improved diagnostic competence, highlighting critical gaps in current agent capabilities.