As AI agents are increasingly deployed in real-world applications, evaluating their reliability in domain-specific technical tasks becomes critical for safe deployment. We present \emph{WirelessBench}, a benchmark of $2,592$ curated problems across three Wireless Communication tasks: HomeWork (WCHW), Network Slicing (WCNS), and Mobile Service Assurance (WCMSA). Our benchmark features a tolerance-aware scoring mechanism that accommodates the precision requirements of engineering calculations, distinguishing acceptable numerical errors from catastrophic unit or formula mistakes. Experiments reveal significant reliability gaps: frontier Large Language Models (LLMs) achieve only $58$-$62\%$ accuracy on computational tasks with direct prompting, while our proposed WirelessAgent achieves $80.65\%$ through workflow optimization. WirelessAgent can avoid prevalent failure modes, including unit conversion errors, order-of-magnitude mistakes, and formula misapplication, by calling domain-specific tools.