Empowering Edge Agents: A Systematic Analysis of Edge-Cloud Collaboration in LLM Agent Execution

Edge–cloud collaboration offers a promising way to combine the lower inference overhead and greater data locality of edge-side execution with the substantially stronger capabilities of cloud-hosted frontier models. However, as LLM agents increasingly operate in long-horizon scenarios, cloud participation becomes interleaved throughout execution rather than a one-shot decision for each query. Such interleaved participation introduces trajectory-level effects that remain insufficiently understood. To address this gap, we introduce EC${}^2$-Bench, a benchmark for evaluating edge–cloud collaboration in long-horizon agent execution. EC${}^2$-Bench provides executable digital-workspace agent tasks and a unified evaluation framework covering task performance, resource usage, and privacy exposure. Using this benchmark, we empirically study diverse forms of edge–cloud collaboration. Our study reveals several key findings. First, edge–cloud collaboration provides an effective execution paradigm for long-horizon agents by achieving a favorable balance among task performance, resource usage, and privacy exposure compared with edge-only and cloud-only execution. Second, as edge models become stronger, collaborative execution approaches cloud-only performance, and performance differences across collaboration strategies become smaller, while privacy exposure remains strongly dependent on the chosen strategy. Third, stronger edge capability changes the preferred collaboration mode, shifting from externally controlled cloud participation and direct cloud execution toward more edge-controlled collaboration. Further analyses identify cloud-invocation decision-making and instruction following as important edge-side abilities underlying such effective collaboration. These findings establish a systematic understanding of edge–cloud collaboration in long-horizon agent execution and provide insights for designing future edge models that can effectively collaborate with cloud models. All code and datasets will be available on GitHub.