Training capable OS agents requires data that simultaneously captures structured user intents, multi-turn task delegation, and grounded tool execution--properties absent from existing datasets. We propose ISE (Intent -> Simulate -> Execute), a three-stage synthesis paradigm that addresses these gaps jointly. Stage 1 constructs roughly 50000 structured intents via a 4D framework (Persona x Domain x Task x Complexity); after deduplication the pool contains 43956 unique intents and attains a Vendi Score of 61.57 over the entire pool on mpnet-base-v2 embeddings (cosine kernel, q=1). Stage 2 drives multi-turn user-agent interaction through a role-locked user simulator that grounds each user turn in actual execution outcomes, producing 23132 complete trajectories averaging 8.12 user turns and 68.24 total dialogue turns. Stage 3 runs every tool call inside a live, isolated OS workspace, generating authentic failure-recovery dynamics instead of simulated responses. Fine-tuning on ISETrace improves ClawEval pass@1 from 19.3 to 37.7 using Qwen3-8B on agent tool-use tasks with a standard protocol. This result outperforms zero-shot GPT-4o and the larger Qwen3-32B base model which is four times bigger. An ablation on Stage 2 proves multi-turn simulation brings a large portion of the performance gain. We release all source code and dataset at https://github.com/Valiere01/ISE-Trace.

翻译：训练高效的操作系统代理需要同时具备结构化用户意图、多轮任务委派和基于实际环境的工具执行等特性的数据，而现有数据集普遍缺失这些属性。我们提出ISE（意图→模拟→执行）三阶段合成范式，系统性解决上述问题。第一阶段通过四维框架（人物×领域×任务×复杂度）构建约5万个结构化意图；经去重后得到43956个独特意图，基于mpnet-base-v2嵌入（余弦核，q=1）对整体池计算Vendi评分达61.57。第二阶段通过角色锁定用户模拟器驱动多轮人机交互，将每轮用户输入锚定于实际执行结果，生成23132条完整轨迹（平均8.12个用户轮次、68.24个总对话轮次）。第三阶段在实时隔离的操作系统工作区执行所有工具调用，产生真实的故障恢复动态而非模拟响应。在标准协议下，基于Qwen3-8B对ISETrace进行微调后，ClawEval的pass@1指标从19.3提升至37.7。该结果超越零样本GPT-4o及规模四倍于本体的Qwen3-32B基座模型。对第二阶段的消融实验表明，多轮模拟贡献了性能提升的主要部分。我们已在https://github.com/Valiere01/ISE-Trace 开源全部源代码与数据集。