Blog Category: Context Management

Technical metadata says what data is. Business context says what it means. Learn why that gap breaks AI agents.

AI-ready data isn’t enough. Agents need AI-ready context: the definitions, runbooks, and institutional knowledge that give structured data meaning.

A context platform is not a data catalog with AI features. Learn the architectural distinction and why it matters for putting AI agents into production.

Ontology vs. semantic layer explained. Learn what each does, where they overlap, and why both depend on context management.

Context graphs and knowledge graphs share the same shape. The real difference is scope and grounding, and it matters more than the vocabulary.

A metadata knowledge graph connects your data assets, pipelines, and meaning. Here’s what it is and why DataHub calls it a context graph.

A phased, practitioner’s guide to implementing an enterprise context layer on the metadata infrastructure you already have.

A context catalog makes metadata usable by AI agents and humans. Learn how it differs from a data catalog.

Context-aware AI agents need more than clever prompts. See why context-awareness is an infrastructure problem, and what production agents actually require.

Why the context layer in financial services starts at the glossary, not the retrieval engine, and what it takes to build one regulators trust.

Everyone’s building a context layer for AI. Most are building the wrong one. Here’s what enterprises actually need.