From Data Quality to Quality of Agentic Data Use: A Conceptual Framework for Agentic Data Engineering

The integration of large language models and autonomous agents into data engineering is shifting data systems from human-specified pipelines to processes in which agents interpret requirements, select sources, generate transformations, invoke tools, and communicate analytical results. This shift introduces a governance gap: a dataset may satisfy established quality requirements and a generated query may execute successfully while the agent applies an incorrect metric, combines incompatible grains, uses unauthorized sources, or produces conclusions without evidential support. We develop a conceptual framework for Agentic Data Engineering, the engineering domain concerned with specifying, validating, governing, and auditing data processes in which AI agents operate under delegated authority. An evidence-informed analysis of six foundations—Data Contracts, Semantic Layers, Data Quality, Guardrails, AI Governance, and Data Provenance—shows that existing controls are essential but fragmented. The framework integrates them through four artifacts: Agentic Data Contracts, Agentic Expectations, Agentic Data Provenance, and Agentic Data Governance, and defines Quality of Agentic Data Use as the central evaluative construct. A lifecycle model, a reference architecture, a failure taxonomy, a multidimensional evaluation scheme, and an illustrative governed sales-analysis scenario operationalize the framework and provide a foundation for the empirical validation of trustworthy agentic data systems.