AI agents can produce incorrect results when they lack sufficient context about the underlying data or business logic. Large language models can generate SQL queries, but they may reference incorrect columns or misinterpret metrics if schemas are unclear. 

Providing well-organized data and a strong semantic layer significantly reduces the likelihood of these errors by grounding the agent in consistent definitions.