Qwen-UI-Agent Technical Report: Toward Next-Generation Real-World Centric Foundation GUI Agents

Abstract GUI agents have the potential to become a general purpose executor over existing digital devices. To advance them toward real-world use, we envision agents that operate reliably on real devices, execute workflows across platforms, combine GUI interaction with CLI execution, complete long-horizon tasks, proactively initiate useful services, and autonomously improve their capabilities with minimal human effort. Guided by this vision, we present Qwen-UI-Agent, a real-world centric foundation GUI agent spanning mobile, computer-use, web, and DeepSearch environments. Qwen-UI-Agent combines diverse sandbox environments with a large-scale real-device mobile runtime. Its unified action space interleaves GUI operations with CLI execution and generates batched actions in a single model turn. An AutoResearch-style data flywheel uses agents to construct tasks and environments, diagnose failures, and plan subsequent iterations. Online RL support training on trajectories exceeding 100 turns, with over 10,000 concurrent environments accelerating rollout. A lightweight harness layer supports proactive service initiation and stateful workflows across mobile and computer.

Introduction. Graphical user interfaces (GUIs) are the primary interface through which humans access digital services and applications. A capable GUI agent (Zhou et al., 2025a; Wang et al., 2025a; Qin et al., 2025; Xu et al., 2026) therefore has the potential to become a general purpose executor over existing application ecosystems: it can understand user intent, perceive application states, operate interfaces, and complete tasks without requiring every service to expose a dedicated API. Recent progress in multimodal foundation models has made this direction increasingly practical, leading to rapid improvements in mobile and desktop GUI agents (Cao et al., 2026; Gao et al., 2026; Huang et al., 2026; Shen et al., 2026). Despite this progress, GUI agents remain largely optimized for simulated benchmarks, leaving a substantial gap between benchmark performance and real-world utility.

Discussion / Conclusion. We presented Qwen-UI-Agent, a real-world centric foundation GUI agent. Guided by a vision of agents that operate on real devices, maintain workflows across platforms, combine GUI interaction with CLI execution, and proactively initiate useful services, Qwen-UI-Agent integrates scalable sandbox environments with a robust real-device mobile runtime, a unified GUI+CLI action space, an agentdriven data flywheel, a training framework combining SFT, action RL, and online RL, and a harness layer for proactive service initiation and cross-platform task execution. Empirically, Qwen-UI-Agent achieves 82.1% on MobileWorld, 92.2% on MobileWorld-Real, and 97.5% on AndroidDaily. It also reaches 79.5% on OSWorld-Verified, and 40.0% on OSWorld-v2. Qwen-UI-Agent further obtains leading or competitive performance across browser use, DeepSearch, and GUI-grounding, while preserving strong general reasoning and agentic capabilities. Together, these results support a systems view of GUI agent development where models, environments, data flywheel, training, and harness are co-designed around real-world usage.