Agentic analytics can be highly reliable when AI agents operate on well-organized data with clear semantic context and sufficient understanding of business logic. 

Large language models are capable of generating SQL, queries and performing multi-step reasoning, but they depend on well-defined schemas and consistent metric definitions. Without this context, agents may produce answers that appear plausible but are incorrect.