尽管2024-2025年主要AI实验室纷纷推出计算机使用原型并在基准测试中取得亮眼成绩，但这些AI代理在真实应用场景中的表现却远低于预期。1在OSWorld-V2基准测试中，最佳模型的任务完成率仅为20.6%，Agents' Last Exam的最佳结果也仅有26.2%。1相比之下，基准测试本身显示的完成率从60.76%到97.4%不等，而人类在WebGames基准上的成功率超过95%。1
问题的根源在于AI代理的工作方式与人类存在根本差异。1GPT-5.5和Claude Opus等模型倾向于通过API调用的方式绕过用户界面，而非像人类一样直接操作界面完成任务。1这种策略导致计算预算的低效配置，视觉定位、低级操作和工具使用的开销占据了代理计算预算的主要部分。1仅通过扩大模型规模无法解决这一核心问题，真正的突破需要改变代理与计算机界面的交互方式。1
Recent analysis reveals a significant gap between how well AI agents perform on standardized benchmarks and their actual effectiveness in real-world computer use scenarios. While major AI laboratories have released computer-use prototypes in 2024-2025 with impressive benchmark results ranging from 60.76% to 97.4% completion rates, the performance drops dramatically in practical applications.1 On the OSWorld-V2 benchmark, the best-performing model achieved only a 20.6% completion rate, while another leading benchmark, Agents' Last Exam, reached just 26.2%.1
The underlying issue stems not from model size constraints but from how AI agents approach interface interaction.1 Rather than directly manipulating user interfaces as humans would, leading models like GPT-5.5 and Claude Opus tend to circumvent interfaces by relying on API calls instead.1 This approach introduces computational inefficiency and reduces reliability. Research into OSWorld 2.0 data shows that agents' computational budgets are dominated by expenses for visual positioning, low-level operations, and tool usage, suggesting fundamental architectural misalignment with human interaction patterns.1 Despite human success rates exceeding 95% on benchmarks like WebGames, AI models remain significantly below this threshold.1
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