Semantic Layer vs Context Layer: Key Differences

This post clarifies the distinction between BI semantic layers and AI context layers. It argues that while semantic layers excel at standardizing business metrics for BI tools, they fall short for AI agents due to limitations in handling vector search, batch refreshes, lack of memory, inability to prevent hallucination, and absence of support for unstructured data. The post defines a context layer as a runtime system that manages information for AI agents at each reasoning step, encompassing retrieval pipelines (RAG), short-term and long-term memory, tool definitions, permission filtering, semantic definitions, and system instructions. It highlights the architectural differences and failure modes of each layer, emphasizing the need for a robust context infrastructure to avoid issues like context rot, fragmentation, and retrieval gaps. Redis Iris is presented as a solution that provides this unified context layer.