A context layer is the infrastructure that assembles business context — schemas, metadata, relationships, documents — into a form AI agents can actually use, reducing hallucinations caused by missing context.
Nexla’s Helix Context Layer ingests documents, Nexset metadata, schemas, and pipeline history into a unified knowledge graph, giving agents structured business context raw data alone can’t provide.
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.
