Reliable Docker-based environment construction is a dominant bottleneck for scaling execution-grounded training and evaluation of software engineering agents. We introduce DockSmith, a specialized agentic Docker builder designed to address this challenge. DockSmith treats environment construction not only as a preprocessing step, but as a core agentic capability that exercises long-horizon tool use, dependency reasoning, and failure recovery, yielding supervision that transfers beyond Docker building itself. DockSmith is trained on large-scale, execution-grounded Docker-building trajectories produced by a SWE-Factory-style pipeline augmented with a loop-detection controller and a cross-task success memory. Training a 30B-A3B model on these trajectories achieves open-source state-of-the-art performance on Multi-Docker-Eval, with 39.72% Fail-to-Pass and 58.28% Commit Rate. Moreover, DockSmith improves out-of-distribution performance on SWE-bench Verified, SWE-bench Multilingual, and Terminal-Bench 2.0, demonstrating broader agentic benefits of environment construction.

翻译：基于Docker的可靠环境构建已成为扩展软件工程智能体执行驱动训练与评估的主要瓶颈。本文提出DockSmith——一种专门设计的智能Docker构建器，旨在解决这一挑战。DockSmith不仅将环境构建视为预处理步骤，更将其作为核心智能能力，通过长周期工具使用、依赖关系推理和故障恢复机制，生成可迁移至Docker构建之外任务的监督信号。该系统采用经循环检测控制器和跨任务成功记忆增强的SWE-Factory式流水线，在大规模执行驱动的Docker构建轨迹上进行训练。基于这些轨迹训练的30B-A3B模型在Multi-Docker-Eval基准上达到开源最优性能，Fail-to-Pass率为39.72%，提交率为58.28%。此外，DockSmith在SWE-bench Verified、SWE-bench Multilingual和Terminal-Bench 2.0等分布外任务上表现出性能提升，证明了环境构建能力对智能体泛化性能的广泛增益。