PromptQL is a data agent designed to improve accuracy when connecting Large Language Models (LLMs) to enterprise data. It addresses the limitations of traditional approaches such as RAG, Text-to-SQL, and function/tool calling by decoupling planning from execution. PromptQL breaks down complex queries into simpler plans, creates detailed query plans with explicit steps for data interaction, and executes these plans programmatically outside the LLM context. This architecture addresses core challenges like context limitations, computation becoming deterministic, and complex cognitive tasks being decomposed into task-specific AI agents. By moving data processing outside the LLM context, PromptQL achieves 100% accuracy and repeatability even with increasing data volumes and query complexity, outperforming traditional approaches in benchmarks.