Anatomy of an Agentic Analytics System: Inside the Multi-Step Reasoning Loop

This article provides a detailed technical breakdown of how agentic analytics systems work, going beyond the simple explanation of 'AI answering questions.' It describes the ReAct (Reasoning + Acting) loop, where the LLM iteratively reasons, acts by invoking tools like SQL execution and schema exploration, and observes results to refine its approach. The article covers tool access design, schema exploration, self-correction mechanisms for handling errors, and multi-agent systems for complex analysis. It also discusses factors that determine agent quality, emphasizing the difference between useful and confidently wrong answers. This is highly relevant to IT/technology, focusing on AI, software engineering, and data analytics.