How to Build Context-Powered AI Agents

Connecting AI to data is a solved problem. Giving it the business meaning to act on that data is the hard part.

Definitions drift, relationships change, and without governance the same term ends up with several conflicting meanings. This workshop companion explains why that makes business context hard to maintain. It compares bottom-up, inference-led context with top-down context built on Industry Knowledge Models, and shows why starting with a governed, reusable model leads to more consistent, transparent, and better-grounded AI responses. You’ll find a plain-English glossary of key terms, including Context Engine, data products, semantic layer, grounding, and RAG, each with an in-practice example. A four-step walkthrough of the customer satisfaction scenario shows how business meaning is created, organized, and applied from first question to final action.