这篇论文采访了20位一线从业者，总结出SE Agent开发的真实流程和六类坑，做智能体项目前建议先看看。
本研究通过对12家组织的20名从业者半结构化访谈和80名从业者在线调查，首次系统揭示SE Agent的构建实践。随着实现成本降低，瓶颈从编码转向需求、协调、审查与部署等非编码工作。论文总结了一个七阶段工作流，并发现评估驱动开发成为新范式——评估迭代指导规格，规格成为人机共享的版本化工件。研究识别了六大挑战，包括不可靠的评估信号、理解债务以及模型更新引发的行为变化。
How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study
The rise of Software Engineering (SE) agents, i.e., LLM-based agents that can understand large codebases and carry out engineering tasks with limited human intervention, has been marked by rapid advances and adoption, but little is known about how developers build these systems in practice: existing studies mine repositories or examine deployment, but few investigate how SE agents are constructed. Through semi-structured interviews with 20 practitioners from 12 organizations and an online survey of 80 practitioners, this paper is the first to study how SE processes are changing in the development of SE agents and what challenges developers face. We find that as implementation becomes cheaper, bottlenecks shift rather than disappear: long-standing non-coding work such as requirements, coordination, review, and deployment becomes more visible, while reviewing and evaluating agent output becomes new and central. We characterize a seven-stage workflow and a shift toward evaluation-driven development, in which evaluation steers iteration and specifications become versioned artifacts read by both humans and agents. We further identify six challenges that teams face, together with the practices they adopt to address them, including unreliable evaluation signals, comprehension debt as code outpaces understanding, and behavioral changes introduced by provider-side model updates.