How to Build Accurate Data Analytics Agents: Ontology, Graph, Evals, and Learning Loops

How do you build a data analytics agent whose numbers you can trust, and then use it for decision-grade analysis?

Agents that answer business questions often sound confident and still pick the wrong table, apply the wrong metric definition, or filter on the wrong value. This talk presents a practical blueprint for treating accuracy as a property of the system, drawn from building an enterprise analytics agent.

First, an ontology of business semantics and definitions in which onboarding is a platform-first primitive, so definitions can be added, edited and refined continuously.

Second, a knowledge graph that gives the agent a governed map of metrics, dimensions and datasources, so it grounds each answer in retrieved definitions rather than inferring them, and resolves the right grain and dimensional cut for each question.

Third, continuous evaluations with golden datasets and extensive online LLM judges, that make accuracy measurable and dependable.

Finally, SME validation and correction loops: continuous monitoring detects accuracy degradation, subject-matter experts (SMEs) correct and validate the affected definitions, and each revision can be re-evaluated before it is trusted, so the agent’s accuracy compounds and its semantic coverage expands over time. With that accuracy foundation in place, investigations, root-cause analyses, and decisions become reliable, because an agent can only explain why a metric moved if it can be trusted on what the metric represents.