The Delegation Test for AI: Why Context Engineering Determines Model Success

AI systems fail when context doesn’t scale. This article explains the limits of context graphs, why static relationships break for enterprise AI, and what’s needed to deliver accurate, trustworthy AI outputs at scale.

Context engineering is the systematic practice of designing and controlling the information AI models consume at runtime, ensuring outputs are accurate, auditable, and compliant.

In episode four of DatAInnovators & Builders, BigID’s Stephen Gatchell explains the data governance gap blocking AI production, why unstructured data breaks legacy models, and how data product frameworks enable scale.