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

2026-08-08

AI Summary

This work addresses the safety risks posed by outdated or inconsistent telemetry data in large language model (LLM) agents for autonomous wireless network operations, which can lead to unsafe actions. To tackle this issue, the authors propose WirelessOptBench—the first benchmark framework specifically designed for evaluating action safety in wireless network management—formulating operational tasks as decision-making scenarios with controllable telemetry faults and action constraints. They further introduce WirelessOpsAgent, which validates and repairs recoverable support failures based on current evidence before executing any action. Experimental results across 600 scenarios using three backbone LLMs demonstrate that the approach achieves an Exact Action Accuracy of up to 0.983. Notably, on Claude Sonnet 4.6, the Unsafe APPLY Rate drops significantly from 82.2% to 10.3%, confirming the effectiveness of the proposed pre-execution support verification and repair mechanism.

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/.

Problem

• action assurance
• wireless networks
• LLM agents
• telemetry consistency
• execution safety

Innovation

• action assurance
• WirelessOptBench
• LLM agent
• telemetry grounding
• execution safety