AI Summary

Enterprise AI currently relies on context engineering, where teams connect enterprise systems, chunk documents, create embeddings, and build retrieval pipelines for each AI application. This method works for isolated assistants but treats knowledge as application‑specific rather than a shared asset. As more AI agents are deployed, different teams process the same documents, maintain separate embeddings and indexes, and produce inconsistent representations of business knowledge. The result is a fragmented knowledge base that undermines reliability across enterprise AI systems. The challenge shifts from merely supplying context to managing enterprise knowledge as a unified resource.

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