Context Is the New Data Layer

What this chapter covers

Storage and compute used to be the layers that mattered. In the agentic era, the layer that matters most is meaning — the shared definitions, metrics, and relationships an AI system needs to answer any real question correctly. This chapter names that layer and shows why it sits above the warehouse, not inside it.

Key takeaways

• Why 'the data is there' rarely means 'the AI can use it'
• The difference between a data layer, a semantic layer, and a context layer
• How business meaning gets lost between the warehouse and the model
• The three things a context layer must expose that a catalog cannot
• A minimum viable context layer you can start building in one sprint