AI data agent
An AI system that answers business questions by querying an organization's data on someone's behalf. Beyond its answers, the pattern of questions it receives reveals what people need to know.
An AI data agent lets people ask questions about company data in plain language. It finds the relevant data, writes and runs queries, interprets the result and returns an answer, often with a chart or explanation. It makes analysis available to people who cannot write queries or wait for an analyst.
Its quality depends on context: business definitions, metrics and relationships, usually supplied by a semantic layer, and on inheriting the user's existing permissions so it cannot reveal data they should not see.
The second output
The obvious output is the answer. The more interesting one is the stream of questions people ask. That pattern is the second dataset: it shows unresolved decision demand, missing context and processes that are still manual. Treat it as a signal about where the organization needs better data, not as a way to monitor individuals.
Evaluate the questions as well as the answers, and use them to prioritize the data team's backlog. This is an application of agentic AI.
Read more in Your AI data agent is creating a second dataset.
Related terms
Second dataset
The record of questions people ask an AI data agent. Unlike the first dataset, which says what happened, it reveals what people need to know and where decision demand and context gaps sit.
Semantic layer
A shared set of business definitions, metrics, calculations and data relationships that sits between raw data and the people or AI agents that query it, so everyone means the same thing.
Agentic AI
AI systems that pursue a goal by planning, using tools and taking multi-step actions with some autonomy, rather than only answering a single prompt.