AI agents require semantic layers and institutional knowledge to reduce errors

AI agents face challenges in accuracy when they lack access to governed business definitions and unwritten organizational knowledge. Without a semantic layer, agents may misinterpret metrics such as revenue or active customer counts, leading to confident but incorrect answers.

Semantic layer tools serve as a bridge between data warehouses and BI tools or AI agents, defining metrics once to ensure consistency across platforms. These tools are categorized into native layers, which operate within specific platforms like Snowflake or Databricks, and universal layers, such as dbt or Cube, which serve multiple tools.

Beyond structured data, a significant gap exists regarding institutional knowledge—the unwritten rules and organizational habits held by experienced employees. To address this, solutions like the Connect AI Gateway aim to create a context layer that captures tacit knowledge and learns from user corrections to improve agentic workflows.