Reproducibility is central to research credibility, yet large-scale reanalysis of empricial data remains costly because replication packages vary widely in structure, software environment, and documentation. We develop and evaluate an agentic AI workflow that addresses this execution bottleneck while preserving scientific rigor. The system separates scientific reasoning from computational execution: researchers design fixed diagnostic templates, and the workflow automates the acquisition, harmonization, and execution of replication materials using pre-specified, version-controlled code. A structured knowledge layer records resolved failure patterns, enabling adaptation across heterogeneous studies while keeping each pipeline version transparent and stable. We evaluate this workflow on 92 instrumental variable (IV) studies, including 67 with manually verified reproducible 2SLS estimates and 25 newly published IV studies under identical criteria. For each paper, we analyze up to three two-stage least squares (2SLS) specifications, totaling 215. Across the 92 papers, the system achieves 87% end-to-end success overall. Conditional on accessible data and code, reproducibility is 100% at both the paper and specification levels. The framework substantially lowers the cost of executing established empirical protocols and can be adapted in empirical settings where analytic templates and norms of transparency are well established.

翻译：可复现性是研究可信度的核心，但由于复制包在结构、软件环境和文档方面差异巨大，对实证数据进行大规模再分析仍然成本高昂。我们开发并评估了一种智能体AI工作流程，旨在解决这一执行瓶颈，同时保持科学严谨性。该系统将科学推理与计算执行分离：研究人员设计固定的诊断模板，工作流程则使用预先指定、版本控制的代码，自动化完成复制材料的获取、协调与执行。结构化知识层记录已解决的故障模式，使得工作流能够适应异构研究，同时保持每个流程版本的透明性与稳定性。我们在92项工具变量（IV）研究上评估该工作流程，包括67项具有手动验证可复现2SLS估计的研究，以及25项在相同标准下新发表的IV研究。对每篇论文，我们分析了最多三种两阶段最小二乘法（2SLS）设定，总计215项。在全部92篇论文中，系统整体实现了87%的端到端成功率。在数据和代码可获取的条件下，论文层面和设定层面的可复现性均达到100%。该框架显著降低了执行既定实证方案的成本，并可适用于分析模板和透明度规范已明确建立的实证研究场景。