Agentic AI is changing software interaction through frameworks for collaboration and autonomy. Metadata underpins the ability of AI to scale effectively, serving as the foundational layer for data management. A lack of a modern metadata strategy can lead to chaos and unreliability in advanced AI systems. Successful AI requires robust metadata infrastructure that includes capabilities to discover, understand, trust, and use data. Automating metadata harvesting and enriching data context with business glossaries enhances usability and ensures AI systems navigate data efficiently.
Modern AI needs more than raw data. It needs context that evolves as new sources appear and applications multiply. This context is reflected in four practical capabilities essential for any robust metadata infrastructure: discover, understand, trust, and use. Discover means navigating billions of objects without tedious manual work. A modern metadata system automates metadata harvesting across diverse data stores, lakes, and third-party databases.
Without a modern metadata strategy, even the most advanced AI systems struggle to find the right data, interpret it correctly, and use it responsibly. Metadata is the key that lets every asset, model, and agent know where it is, how it's found, and what rules apply.
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