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Making AI agents trustworthy starts with domain-scoped context and evals. Lessons from 5 months of beta as DataHub Cloud Context goes GA.

Context Graph vs. Ontology: Why Each Needs the Other
Context graph vs ontology: an ontology defines meaning, a context graph keeps it true. Why each needs the other and how to start small.

What Is a Unified Context Layer? (And What Unified Doesn’t Mean)
A unified context layer is one governed context graph for every person and agent. What unified means, and what it doesn't.

Data Lifecycle Management: The Six Stages for the AI Era
Data lifecycle management now runs as a loop, not a line. See the six stages, why AI changed them, and what has to travel with your data.

Data Lineage for AI: What Changes When Agents Read Your Lineage Graph
Data lineage for AI has two jobs: routing agents before they answer and tracing answers after. Learn why accuracy now matters most.

Context Layer for AI Agents: What It Is and the Three Tests It Has to Pass
A context layer for AI agents has to be shared, stay current, and prove it works. Here are the three tests yours should pass.
