Data teams are at very different points in their AI journey — some are already building agentic workflows and running into hard questions about governance, centralization, and organizational fit; others are still waiting for approval. The challenges look different from where each organization stands, but the solution is the same: a governed, centralized platform powered by agentic AI processes, validating data at scale with documented, transparent results leading to data trust.

For organizations running complex reconciliation projects, managing ongoing data quality monitoring, or validating data across a migration, the stakes of getting this wrong are high. Ungoverned automation creates the illusion of coverage without the auditability, consistency, or human oversight that regulated industries and data governance programs actually require.

In this session, Jonathan Agee and Matt Agee walk through how the Validatar platform puts governed agentic AI into practice — using metadata-driven test generation to automatically profile and scale validation across thousands of tables, automated source-to-target reconciliation to verify data integrity across systems, native integration with data catalogs to keep quality aligned with your governance framework, and continuous monitoring across development, QA, and production environments. Drawing on real-world implementations across insurance, healthcare, and financial services — including one organization that validated 2,300 tables in a single week and another that uncovered 12 previously undetected critical defects across 20,000 columns — they'll share a practical framework you can bring back and apply immediately.

Takeaways:

• Why governance must be designed into agentic AI workflows from the start — not added after something breaks
• How metadata-driven agentic automation scales testing and reconciliation without sacrificing auditability
• What a governed monitoring framework looks like across development, QA, and production
• How to build the case internally for governed AI automation of data quality validation with data governance and compliance stakeholders
• Real metrics from regulated-industry organizations applying this approach at scale

Speaker: Matt Agee, VP of Product, Validatar

Matt Agee is VP of Product at Validatar, where he leads product strategy and drives technical solution development for some of the most complex data quality implementations in regulated industries. With deep expertise in data architecture, automated testing frameworks, and cloud data platforms, Matt works closely with engineering and data teams to design governed agentic workflows that scale across thousands of tables without sacrificing auditability or control. He brings both technical depth and real-world implementation experience to the question of what it actually takes to trust your data at scale.

Speaker: Jonathan Agee, Founder & CEO, Validatar

Jonathan Agee is an accomplished data and analytics leader with a passion for data-driven innovation. Jonathan's expertise lies in seeing the big picture and understanding how diverse technologies work together to make a cohesive solution. Prior to founding Validatar, a pioneering platform for Data Quality Assurance, he led numerous data teams and projects, developing a deep understanding of data management complexities and solutions. Jonathan is passionate about automation and gets excited every time there is an opportunity for data to unlock innovative business models and generate unprecedented value. Born and raised in Oaxaca, Mexico, Jonathan now resides in Dallas, TX with his lovely wife and family. His passions extend beyond the tech world, finding joy in RC planes, nurturing his pandemic garden, and relishing tacos al pastor.