How to Build an AI Data Agent Over Your Existing Data Warehouse

Build an AI data agent by giving it a specific analytical job, a controlled way to query data, and a way to show the evidence behind its answers. You can often start with the warehouse and modeling tools you already operate.

For this walkthrough, the job is to answer a sales manager's questions about net merchandise sales by country. The agent can query and explain results. It cannot change source records or send messages to customers.

Define the task before connecting a model

Write down the supported questions, metric definitions, allowed dimensions, reporting calendar, and user groups. Decide what the agent should do when information is missing or a question is outside scope.

For example, “sales last month by country” is supported once the metric and dates are resolved. “Which campaign caused the decline?” requires additional evidence and an analytical method; the agent should not infer the cause from the sales table alone.

Start with an approved semantic metric. A small, coherent domain is easier to test than an entire warehouse whose business definitions are still disputed.

Build the query path

A practical architecture is:

Authenticated user
  → application and task scope
  → agent selecting an approved metric operation
  → query service enforcing access and validating arguments
  → warehouse calculation
  → result with evidence metadata
  → explanation shown to the user

The model chooses an operation; the service enforces whether it is allowed. Derive identity from authentication rather than a customer or tenant identifier supplied by the model. Use database credentials and policies appropriate to the user's permitted scope.

An existing semantic platform may already provide parts of this path. For example, the dbt MCP integration offers access to dbt context and semantic metrics, while Omni's MCP server provides an interface for external AI tools. Check the selected integration's available operations and authorization behavior before enabling it.

Return more than a number

This illustrative application response contains the information needed to explain and investigate a result. It is not a vendor API schema:

{
  "metric": "net_merchandise_sales",
  "definition_version": "sales-v1",
  "period": {"start": "2026-08-01", "end_exclusive": "2026-09-01"},
  "currency": "USD",
  "data_as_of": "2026-09-08T10:00:00Z",
  "query_id": "example-query-001",
  "rows": [{"country": "Example market", "amount": 140.0}]
}

The answer should preserve the period, units, and definition. Keep any sensitive query details in access-controlled operational records rather than exposing them to every user.

Handle a missing-data case explicitly

Suppose the daily order ingestion has failed, but the agent still sees a valid sales table. A normal query might return a low number and produce an alarming explanation.

Add freshness and completeness checks before analysis. If the required data is incomplete, identify the affected period and stop the comparison or label the result as partial. A successful database query does not establish that all expected records arrived.

If the warehouse times out, distinguish that from a legitimate empty result. Retry only within the task's budget and return an unresolved status when the dependency remains unavailable.

Test the full interaction

Use a fixed fixture with known totals and a representative set of permitted business questions. Include alternate phrasings, ambiguous dates, restricted users, unsupported dimensions, stale data, and query failures.

Check metric selection, numeric results, access enforcement, and explanation support separately. Repeat selected questions to observe variation. Record a baseline before changing the model or adding tools.

Begin with a small user group and an owner for disputed answers. Review traces under appropriate permissions, correct the underlying definition or tool behavior, and add each resolved failure to the regression set.

The next step can be an evidence-based reporting agent, using the same approved operations. Labs4Change can help implement your first data agent around a clearly scoped business question.