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# Enterprise Context Engineering

**[Materialize](https://daily.dev/sources/materialize)** · 7 min read · 0 upvotes · 0 comments

## Summary

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.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://materialize.com/blog/enterprise-context-engineering/>

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---

Tags: [#agentic-ai](https://daily.dev/tags/agentic-ai), [#big-data](https://daily.dev/tags/big-data), [#context-engineering](https://daily.dev/tags/context-engineering), [#materialize](https://daily.dev/tags/materialize)

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