TeleAgent-Logs: Benchmarking Evidence-Grounded Agents for Interactive 5G Troubleshooting

Large language model agents offer a promising way to automate telecommunications network operations, improve efficiency, and deliver better services to end users, yet realistic benchmarks for network troubleshooting remain scarce. In telecommunication networks, effective diagnosis requires more than predicting a root cause: an engineer must identify where and when degradation occurs, analyze heterogeneous network logs, gather supporting evidence, and determine actionable remediation. We introduce TeleAgent-Logs, an agentic benchmark that reformulates network fault diagnosis and remediation as an interactive decision-making problem. Each task is based on a network drive test and exposes five complementary data sources: user-plane data, signaling-plane data, measurement-report data, network-configuration data, and traffic data—through a fixed set of diagnostic tools for both data access and radio and network analysis. Agents must decide which observations to inspect, which tools to invoke, and how to combine their results into structured fault hypotheses covering four capabilities: localization, fault identification, evidence grounding, and remediation. We consider two evaluation settings that require either one or all evidence-supported fault hypotheses, and introduce taxonomy-aware matching together with component-wise evaluation of each troubleshooting stage. By shifting evaluation from static answer selection to sequential investigation, analysis, and corrective actions, the benchmark measures the full troubleshooting loop and provides a challenging testbed for tool-using agents in 5G network operations. We evaluate four models across five harnesses and observe substantial performance variation across model–harness combinations. Overall, agents are better at locating degradations and identifying faults than they ground diagnoses or propose correct remediation. Finally, our comparison across fault categories highlight systematic confusion between related radio faults such as antenna tilt, transmission power, and mobility configuration.