Why You're Doing Context Engineering Wrong
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Context engineering — the practice of feeding AI agents the right data at the right time — is widely misunderstood. Common mistakes include stuffing large context windows with raw data (causing context confusion, poisoning, distraction, and clash), ignoring latency budgets, and failing to keep vector metadata fresh. The real fix is a live data layer architecture that pre-computes and continuously maintains curated business objects before they reach the context window. This eliminates stale data, reduces token waste, and resolves latency tradeoffs. Materialize is presented as a SQL-based live data layer that enables incremental view maintenance, surgical vector updates, and millisecond-fresh context for production AI agents.
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How we are doing context engineering wrongThe right way to do context engineeringMaterialize: Live data architecture for AI context engineeringNext stepsSort: