Enterprise AI teams are learning that better models alone don't improve outcomes — context does. Fine-tuning falls short because enterprise knowledge is dynamic and distributed. RAG improves grounding but only helps models find information, not truly understand systems. What's needed is a dedicated enterprise context layer that continuously ingests organizational knowledge, connects data sources into relationship graphs, and delivers relevant context dynamically at runtime. Without this layer, AI agents cannot reliably enforce architectural standards, navigate dependencies, or execute end-to-end workflows — they're essentially guessing.
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