Agentic AI systems can only reason, retrieve, and act as well as the data layer beneath them allows. In enterprise environments, agents often fail not because the model is weak, but because the data is incomplete, poorly governed, out of context, or difficult to audit. This session explores how teams can build the data foundations needed for reliable agentic AI, covering pipelines, permissions, metadata, lineage, governance, and feedback loops. Key takeaways: →Why agent failures often start in the data layer. →What production-ready agents need from enterprise data systems. → How governance, lineage, and permissions improve agent reliability. → How to identify weak points before deploying agents into real workflows.