Semantic Layer vs Context Layer: Key Differences

What this post added

This post clarifies the distinction between BI semantic layers and AI context layers. It details the limitations of semantic layers for AI agents, including lack of native vector search support, batch refresh, no memory, inability to prevent hallucination, and no support for unstructured data. It then defines a context layer as a runtime system that manages AI agent access to information at each reasoning step, encompassing retrieval pipelines (RAG), short-term and long-term memory, tool definitions, and permission filtering. The post highlights the architectural differences and failure modes of each layer, emphasizing the need for a dedicated context infrastructure to avoid issues like context rot, fragmentation across data stores, and retrieval gaps. Redis Iris is presented as a solution that provides fresh context and long-term memory for AI agents.