Context engineering is becoming a critical discipline for enterprises building agentic AI systems. Learn how data platform teams can overcome LLM limitations, rising infrastructure costs, and fragmented context strategies—and how Materialize enables scalable, real-time context systems for production AI applications.

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Context engineering is emerging as a critical discipline for enterprises building agentic AI systems. Two key constraints drive this: LLM context window limitations (due to transformer architecture's n² attention complexity) and rising memory costs from AI-driven chip shortages. Data platform teams will need to build production-grade context systems that transform business data into distilled semantic definitions, are cost-effective via incremental computation, serve data sub-second, and guarantee correctness. Materialize is positioned as a solution, using incremental computation and materialized views to build an operational data mesh that feeds live, consistent context to AI agents. A case study with Day AI illustrates how this approach enables a small team to maintain a fresh, queryable context layer for an AI-native CRM.

Enterprise Context Engineering

How Materialize powers Enterprise Context Systems

Production Context Engineering Case Study