WirelessOpsAgent: A Benchmark and Agent Design for Action Assurance in Wireless Networks

Abstract

Large language model (LLM) agents are emerging as planners for autonomous wireless network operations. Yet a task answer that is correct at proposal time can still be unsafe at execution time if supporting telemetry is stale or inconsistent. Existing benchmarks mainly evaluate task solving from fixed observations and leave support checking at execution time untested. We introduce WirelessOptBench, a benchmark for action assurance in wireless operations. It turns wireless tasks into execution state decision episodes with controlled telemetry faults and action constraints. We further develop WirelessOpsAgent, which grounds candidate actions in current evidence and repairs recoverable support failures before execution. Across three backbone evaluations with 600 episodes each, WirelessOpsAgent achieves up to 0.983 Exact Action Accuracy. On Claude Sonnet 4.6, the Unsafe APPLY Rate decreases from 82.2% to 10.3% relative to the safest baseline. We make WirelessOptBench available at https://anonymous.4open.science/r/wirelessopsbench-artifact-D969/.