Most data catalogs were built for human analysts, not autonomous agents. As AI shifts from prototypes to production, the limitations of passive, search-based metadata are becoming an enterprise-wide bottleneck. This white paper explores why 80% of AI initiatives stall due to foundational data issues, and how agentic AI demands a new kind of metadata: machine-actionable, semantic, governed, and contextual.

Inside, you’ll discover:

• The top four gaps legacy catalogs can’t close
• What “context collapse” means for AI trust and performance
• How operational metadata enables faster, safer, and more scalable AI
• A blueprint for building AI-ready metadata infrastructure with TopBraid EDG