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What is agentic analytics?

5 min readUpdated

Agentic analytics describes AI systems that plan and carry out multi-step analysis against enterprise data rather than answering one question at a time. It differs from conversational BI in that the system decides its own sequence of steps, and it differs from a dashboard in that nothing was pre-built. The governance implication is that controls must be enforced on each executed query, because no human reviews the intermediate steps.

Three things that get called the same name

The distinction matters for governance rather than for marketing. In the first two, a person sees every query's result. In the third, intermediate results feed the next step without anyone looking at them, so an error at step two arrives as a confident conclusion at step five.

What the user doesWhat the system does
DashboardReads a pre-built viewRuns a query someone wrote earlier
Conversational BIAsks one questionTranslates it, executes, explains
Agentic analyticsStates a goalPlans steps, runs several queries, reasons over results

Where it earns its keep

It earns its keep on questions that decompose. "Why did margin drop in EMEA?" is a multi-step investigation: check volume, then mix, then discounting, then a specific account. That is tedious for a person and well suited to a system that can hold the thread.

It helps less on questions with one obvious query behind them, where the extra machinery adds latency and failure modes without adding insight.

What to require before turning it on

  • Per-query enforcementEvery step the agent runs is a real query against real data, and each one has to carry the same restrictions as a query a person typed.
  • Visible reasoningThe steps taken, and the query behind each, have to be inspectable after the fact. A conclusion whose derivation cannot be replayed cannot be checked.
  • A real refusal pathMulti-step reasoning multiplies the opportunity to substitute something plausible for something absent. The system has to be able to stop.