From natural-language questions to an evaluable data agent

A data agent can turn a natural-language question into queries, tables and charts, but that capability creates value only when permitted sources and expected answers are explicit. Naming the project, datasets and tables limits access and reduces the chance of querying an unrelated source.

Three steps for a pilot

First, collect ten to twenty real user questions and prepare reviewed reference answers. Next, give the agent business context such as metric definitions, table relationships and time constraints. Finally, evaluate numerical accuracy, source selection, response time and traceability.

Data agents can support sales operations, dynamic reporting or an embedded software experience. Their tool and action scope should still match the risk. In early stages, let the agent recommend while a person approves consequential actions. This separates the convenience of natural language from the harder question of whether the system is reliable enough for a specific workflow.

Source: Google Cloud — Building a conversational agent in BigQuery