Agentic analytics

Agentic analytics describes analytics work carried out by an AI agent: a system that, given a goal such as explaining why signups dropped last week, can independently decide which data to query, run the analysis, check its own intermediate results, and produce a finished explanation or report, rather than waiting for a person to specify each query.

It extends augmented analytics, where AI assists a human analyst at each step, by removing the human from most of the intermediate steps and having the agent plan its own path through the data, typically using natural language query to translate the goal into actual queries, calling a semantic layer or database, and iterating if a result looks incomplete or contradictory. This differs from an AI copilot, which drafts suggestions for a person working interactively, rather than executing the analysis end to end.

Agentic analytics matters because it can compress investigative work, root-causing a metric change, drafting a first-pass report, that used to take an analyst hours of manual querying. The main risks are the same as any autonomous agent: an early wrong turn, a bad query or a misread result, can propagate through the rest of the analysis unnoticed, so agentic analytics outputs generally need the same scrutiny as decision intelligence recommendations, not blind trust.