Agentic AI offers a transformative approach to data management by enabling AI systems to perform self-directed tasks, make predictive decisions, and understand context independently. As businesses face increasing data volumes and the need for rapid insights, agentic AI emerges as a solution to modernize data operations, shifting from reactive to proactive management. Key vendor solutions like Acceldata, Informatica, and Monte Carlo employ intelligent agents to automate data tasks, enhance quality, and enable real-time decision-making. These platforms offer features such as data observability, automated error detection, and workflow optimization, reducing manual workload and promoting data reliability. Despite its benefits, implementing agentic AI poses challenges, including ensuring data quality, integration with existing systems, and justifying cost versus return on investment. Real-world applications demonstrate its impact in areas like proactive data monitoring and maintaining data quality, with agentic AI systems offering autonomous problem-solving capabilities that enhance operational efficiency and decision-making. As the demand for intelligent data management grows, selecting the right vendor involves evaluating scalability, integration, ease of use, and support to ensure a sustainable partnership.