A Governed Human–AI–Agent Workflow for Specialized Data and Data Engineering Agents: Authorization, Verification, and Provenance

Large language model agents are increasingly used to analyze data, generate code, and execute operations in data-engineering environments. However, successful technical execution does not guarantee that an operation is authorized or that its results are correct and verifiable. This creates governance challenges when specialized agents modify persistent data. This study presents a Governed Human–AI–Agent Workflow that coordinates a Data Expert Agent and a Data Engineering Agent under human supervision. The objective is to establish explicit responsibilities for planning, authorization, execution, verification, and provenance. The workflow was developed using an adapted Design Science Research Methodology and demonstrated through a controlled Data Lakehouse experiment using synthetic academic data. Documented execution failures were analyzed to identify governance requirements and develop operational controls. These failures included repeated execution, inconsistent evaluation units, premature remediation, and unintended effects across data-quality rules. The resulting artifact comprises nine stages and four prompt-based contracts: Planning, Execution, Verification, and Refinement. An initial intervention increased the data-quality detection F1 score from 0.8084 to 0.9077 but introduced 49 false negatives. A subsequent authorized refinement increased F1 to 0.9988, with one remaining false negative. These results illustrate the importance of verifying the effects of technical interventions before accepting a new persistent data state. The contribution is a process-oriented governance artifact that integrates human authorization, specialized agent responsibilities, bounded execution, read-only verification, and persistent evidence. The study provides a formative demonstration within one controlled academic laboratory scenario. Independent evaluation across other datasets, platforms, and institutional environments remains necessary.