What you get with Empromptu Data Agent
Natural language to SQL
Plain-language questions translate directly to accurate, warehouse-native SQL.
- Understands your schema context automatically
- Handles JOINs, aggregations, and CTEs
- Explains queries in plain language
Analytical memory
The agent remembers past queries and refines its understanding over every session.
- Schema context improves with usage
- Remembers business logic and naming conventions
- No retraining required
Governed access
Row-level security and role-based access so the right people see the right data.
- Integrates with existing IAM policies
- Full query audit trail
- PII masking out of the box
Table of Contents
How a Data Agent Works
A data agent operates in three steps: (1) parse the natural-language question and map it to your specific schema and business definitions; (2) write the SQL query that answers the question, handling joins, filters, and aggregations correctly; (3) run the query, validate the output, and return the answer with relevant caveats in the channel where the question was asked. The agent understands your business semantics — the difference between your "revenue" and finance's "GAAP revenue" — because it was trained on your specific schema documentation and business glossary, not on generic SQL patterns.
Data Agent vs. BI Tools: When to Use Each
Data agents and BI tools serve different primary use cases. BI tools (Tableau, Looker, Power BI) excel at recurring, structured reporting: board-level dashboards, regulatory reporting, and operational metrics that stakeholders need on a predictable cadence. Data agents handle the 80% of analytical demand that is ad-hoc, exploratory, and perpetually underserved by a dashboard library. The right architecture for most enterprise teams combines both: a maintained BI layer for governed recurring reporting and a data agent for the analytical queue. Neither fully replaces the other.
Text-to-SQL: The Core Technology
The core technology powering data agents is text-to-SQL: the ability to translate a natural-language question into a valid SQL query against a specific database schema. Generic text-to-SQL (LLM with a warehouse connector) fails on complex schemas because it doesn't understand your business semantics — it guesses at join conditions, misses business-logic filters, and produces queries that return technically correct but analytically wrong results. Purpose-built data agents solve this by training on your specific schema documentation, join relationships, and business glossary before they answer any questions.
Data Agent Use Cases
Common data agent use cases include: (1) ad-hoc revenue analysis — "what drove the Q2 revenue change across segments?"; (2) operational monitoring — "which accounts have had no activity in 60+ days?"; (3) self-service reporting for non-technical stakeholders who cannot write SQL; (4) anomaly investigation — "why did conversion rate drop 8% this week?"; (5) competitive benchmarking and cohort analysis. The common thread is that these questions are too ad-hoc for a standing dashboard and too frequent to route to an analyst every time.
How to Evaluate a Data Agent
Key evaluation criteria for a data agent: (1) schema fidelity — does it understand your specific table relationships, not just generic SQL patterns?; (2) business semantics — does it know your revenue definitions, not just database fields?; (3) answer accuracy on your hardest 10 real questions before you sign anything; (4) channel integration — Slack, email, or embedded chat?; (5) query transparency — can stakeholders see the SQL behind every answer?; (6) data governance — what data can the agent access and who controls that permission boundary?
Explore the wider picture
Connected topics worth reading next
- cpq softwareCPQ software replacementData agents are the analytics-side answer to the same pattern playing out in CPQ: replace rule-engine systems with AI-native platforms that learn from production usage. Teams evaluating BI replacement should know the equivalent move on the revenue-operations side.CPQ Software: The Complete Guide
- salesforce cpq alternativesSalesforce CPQ alternativesMany enterprises evaluate BI replacement and quote-to-cash replacement on the same RFP cycle — both decisions touch the revenue stack and both are forced by per-seat-pricing economics. Worth understanding the CPQ side if you're evaluating BI.Salesforce CPQ Alternatives in 2026
Frequently asked questions
- What is a data agent?
- A data agent is an AI system trained on your company's specific data warehouse and business semantics that answers analytical questions in natural language — by writing the SQL, running the query, and returning the result — without requiring a dashboard to be built first. Unlike BI tools, a data agent handles ad-hoc questions on demand, in the channel where the question was asked.
- How is a data agent different from Tableau or Looker?
- Tableau and Looker require an analyst to build a dashboard before a question can be answered. A data agent answers questions directly by writing and running the SQL itself. BI tools are better for recurring structured reporting; data agents handle ad-hoc analytical demand. Most enterprise teams benefit from using both rather than replacing one with the other.
- What is text-to-SQL and is it accurate enough for production?
- Text-to-SQL translates natural-language questions into SQL queries. Generic text-to-SQL using off-the-shelf LLMs fails on complex schemas because it doesn't understand business semantics or join logic. Purpose-built data agents solve this by training on your specific schema documentation and business glossary before answering any questions — which is what makes production accuracy achievable.
- Does a data agent replace the data team?
- No. A data agent eliminates the low-value routing work — stakeholders asking routine questions that an analyst has to translate into SQL and dashboard views. It does not replace the judgment, interpretation, or strategic analysis that a skilled data team provides. The goal is to free analysts from the queue so they focus on the work that requires human expertise.
- What data warehouses does Empromptu's data agent work with?
- TODO: operator to confirm current supported warehouse list before publishing. Generally: Snowflake, BigQuery, Redshift, and Databricks are the most common targets for enterprise data agent deployments. The agent is custom-built per engagement, so warehouse compatibility is confirmed during the scoping process rather than being a product-level constraint.
