Context Engineering for Enterprise AI: Architecting Persistent, Governed Intelligence in Regulated Industries

Abstract

Large language models demonstrate powerful reasoning capabilities across diverse domains. Enterprise deployments face persistent challenges when scaling beyond isolated use cases. Most implementations fail due to inadequate context management and governance frameworks. Traditional prompt-centric designs treat models as stateless reasoning engines without persistent memory. Each interaction starts fresh without access to historical decisions or institutional knowledge. Regulated industries require explainability, auditability, and jurisdiction-aware compliance. Current architectures lack systematic mechanisms to enforce these requirements. This article introduces Context Engineering as a foundational architectural discipline for enterprise artificial intelligence systems. Context becomes a first-class system component rather than prompt-level input data. The framework integrates structured and unstructured enterprise data through controlled memory stores and permissioned pipelines. Governance mechanisms are embedded directly into context construction and validation processes. Compliance rules operate proactively during context assembly rather than through reactive validation. Enterprise insurance implementations demonstrate improved decision consistency and reduced hallucination rates. The architecture enables scalable human-artificial intelligence collaboration while maintaining regulatory compliance. Intelligence emerges as an engineered system property grounded in persistent context rather than solely as model capability.