Use subagents with dbt Wizard in the dbt platform Beta

Subagents let dbt Wizard spin up focused agents to handle parts of a larger task, such as exploring your project, making changes, or reviewing results. dbt Wizard orchestrates them and consolidates the results back into your session.

Use subagents when you can split a task into independent pieces of work. They're useful for reviewing a large pull request, debugging a failed job, adding tests across multiple models, or researching documentation while another agent inspects your project.

For smaller, direct tasks, you usually don't need subagents. Ask a single question, like "What does this model do?" or "Fix this failing test," without splitting the work across agents.

Subagents use more tokens than handling the same task in a single-agent session. Use them when the task benefits from dividing the work.

Share your feedback!

We'd love to hear how dbt Wizard is working for you. Share your feedback by either running the /feedback slash command in your interactive terminal session or by going to the #dbt-wizard channel in the dbt Community Slack.

Thanks so much for your help in improving dbt Wizard and dbt data development!

How subagents work

An agent is a role that describes a type of work, like explorer, worker, or test_writer.

A subagent is a running instance of one of those roles. For example, if you start two explorer agents to answer two different questions, you have two subagents that share the same agent role.

dbt Wizard handles orchestration for you. It starts subagents, routes work to them, waits for their results, and consolidates their output into your session.

You can use subagents in the following ways:

How subagents start	Example
You ask to split up the work.	Review PR #123. Use one agent to map what changed, one to check tests and downstream impact, and one to look up the relevant dbt docs.
dbt Wizard automatically uses a configured agent because your task matches that agent's description.	Add tests for stg_customers and check whether similar staging models are missing tests.

Built-in agents

dbt Wizard ships with built-in agents that it can spawn without any configuration. Several are purpose-built for dbt work:

Agent	What it's for	Example
explorer	Answers specific, well-scoped questions about your project. Fast and read-only — spawn several in parallel for independent questions.	Use explorer to explain what depends on fct_orders.
worker	Performs execution and production work, such as implementing part of a feature, fixing tests or bugs, or splitting a large refactor into independent chunks.	Use worker to update these staging models to follow our naming convention.
validation	Provides dbt validation. After model edits, runs structured validation — SQL check, dbt run with --defer, prod vs. dev comparison, and impact analysis — to validate changes before you merge.	Use validation to check whether my changes to int_payments are safe to merge.
test_writer	Improves dbt test coverage. Analyzes project metadata and warehouse data to find coverage gaps, validates assumptions with queries, and writes schema.yml tests for models with low or no coverage.	Use test_writer to add tests to stg_customers.

You don't need to declare these — dbt Wizard routes to them automatically when a task fits, or you can ask for one by name.

Related docs

• About dbt Wizard in the dbt platform
• dbt Wizard in Studio IDE
• Use subagents locally
• How dbt Wizard works

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