Operational Analytics AI Agent

“How much did we close in May?” is a simple question that, in a company with several systems, eats half a day: export from the CRM, reconcile with accounting, roll it up in a spreadsheet. The operational analytics agent reconciles data across systems, answers such questions in natural language and prepares regular reports — with a mandatory source for every number.

What it does

It pulls data from CRM, accounting systems, ad accounts and spreadsheets via their APIs and reconciles it into consistent metrics through one dictionary. It turns a plain-language question into a deterministic query against the data and states the answer with a number and a source. On a schedule it builds regular summaries and highlights deviations from the norm. Instead of a manual export per question — an answer in seconds, with the option to drill the figure down to the row.

Where the line is

The main risk of an analytics agent is a plausible invented number. So the answer is always built on the result of a query against your data, not the model’s “memory,” and every number carries a source: system, period, filter. No data — the agent says so. And the line runs along the decision: the agent prepares the number and summary, the human acts on it, seeing where the figure came from.

This is the core of enterprise automation — taking manual report assembly off people. When sources are many and have to be reconciled with each other, a multi-agent system runs under the hood: separate agents per source system feed into one combined answer.