An Agentic Retrieval Framework for Autonomous Context-Aware Data Quality Assessment
Data quality assessment is a critical prerequisite for effective data analytics and data-driven decision-making, yet it remains a challenging task due to the inherently context-dependent nature of data quality. Existing approaches often rely on static rules or manual assessment strategies, limiting their adaptability to diverse usage scenarios and constraining automation at scale. Recent advances in artificial intelligence, particularly large language models, offer new opportunities for automating data quality assessment, but raise concerns related to reliability, grounding, and execution safety. In this paper, we propose a unified agentic-retrieval framework for autonomous context-aware ...
| Authors | Hadi Fadlallah, Ibrahim Dhaini, Fatima Mubarak et al. |
|---|---|
| Primary category | cs.DB |
| Published | 15 May 2026 |
| Last updated at source | 15 May 2026 |
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