Agentic AI data quality monitoring revolutionizes traditional data monitoring by autonomously detecting anomalies, reasoning with context, and executing corrective actions, thereby enhancing data integrity and performance. Unlike conventional systems that merely send alerts, agentic AI employs a detect-decide-act model to proactively manage data quality issues across complex ecosystems, including data warehouses, lakes, pipelines, and business intelligence layers. By leveraging machine learning, lineage-aware analysis, and policy-driven automation, it identifies root causes, remediates issues in real time, and learns from past resolutions to improve future accuracy. The approach ensures critical datasets are reliable and compliant, reducing downtime and operational costs across various industries such as finance, healthcare, retail, and marketing technology. Acceldata's platform exemplifies this innovation by combining AI with active metadata, enabling enterprises to transform monitoring into a proactive governance framework that boosts trust and efficiency in data products.