How context engineering makes or breaks AI-driven self-service

This post introduces and elaborates on the concept of 'context engineering' for AI agents in analytics. It defines context as business knowledge that enables AI agents to understand specific data environments, distinguishing it from model capabilities. The post details four key principles for effective context engineering: leveraging existing work, curating context flexibly, strategic rollout of self-serve, and employing a hybrid architecture. It also describes four specific tools within Hex for managing context: warehouse metadata, endorsements, rules files, and semantic models. The core contribution is framing the data team's role as shifting from answering one-off questions to building a 'context lake' that fosters a virtuous cycle of learning and self-service.