What is Context Engineering?

Context Engineering is the practice of designing and operationalizing business meaning, data lineage, quality signals, and policy constraints so AI systems can reliably understand and act on enterprise data. Unlike prompt engineering (which focuses on how questions are asked), Context Engineering establishes what AI systems know before questions are posed. It comprises four core components: semantic context (business definitions), lineage context (data flow and dependencies), operational context (quality and reliability signals), and policy context (compliance and usage constraints). This foundation is critical for Agentic AI systems that reason and act autonomously, enabling them to assess risk correctly, explain decisions, and know when to escalate. Enterprises should prepare by inventorying critical data, unifying metadata into a single context layer, and exposing context through APIs for AI agent consumption.