Types of semantic layers explained: Native, composite and universal approaches
By Pratik Jain, Senior Director of Technology at Kyvos Insights
AI systems do not consume business logic the way traditional dashboards do. If the context is missing or inconsistent, AI agents respond using whatever semantic signals are available, inferred or retrieved. The outputs sound authoritative, but they can be factually incorrect.
Most AI architecture conversations among CTOs focus on model selection, inference, infrastructure and retrieval pipelines. A semantic layer for AI rarely gets the same attention. Gartner says that ignoring semantics makes AI agents inaccurate and inefficient, leading to wasted budget and higher security risks. By contrast, it projects that by 2027, prioritizing semantics will boost agentic AI accuracy by up to 80% and cut costs by up to 60%.
Three architectural approaches define how most enterprises govern business meaning today: native, composite and universal semantic layers. Understanding what each does and where each breaks is a prerequisite to making...
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