Meet Antigravity: Google’s agentic IDE enters the dbt orbit

There’s a new player in the Agentic IDE space, and it’s coming in hot.

Enter Antigravity, Google’s entrance into the world of AI-powered development environments. I spent some time with it this past weekend. Paired it with Gemini 3. Let’s just say…I did not expect to be that impressed. The power jump compared to traditional IDE workflows (and some other popular agentic IDEs) is significant, especially when you bring it into the dbt universe.

Let’s break down what it is, how it fits with dbt, and a few pro tips to get the most out of it.

What is Antigravity?

At its core, Antigravity is a fork of Visual Studio Code. That’s great news because it means most of the extensions, tooling, and workflows you already love just work out of the box.

But Antigravity isn’t “just VS Code with a new coat of paint.” It’s built for an agent-first experience. You’re not just coding, you're collaborating with AI agents that can reason across your project, propose plans, and execute tasks.

Think less autocomplete.Think more “co-pilot who drank three espressos and read your entire repo.”

Getting started with dbt in Antigravity

If you’re working with dbt, step one is easy: install the official dbt extension.

You can read about it here: https://docs.getdbt.com/docs/about-dbt-extension

With the dbt extension installed, you immediately get:

• Column-level lineage
• Query preview
• Rich dbt-aware IDE features
• Improved navigation across models, sources, and tests

In other words, your IDE‌ understands dbt instead of just politely pretending to.

From there, you can immediately start using the built-in agent to help generate SQL and YAML files. The agent scaffolds models, adds tests, and writes the YAML documentation you keep forgetting.

Turning it up: Add the dbt MCP server

If you really want to unlock the power of Antigravity, install the dbt MCP server.

Inside the agent window:

1. Click the three dots in the top-right.
2. Select MCP servers.
3. Add the dbt MCP server.

Eventually, the dbt MCP server will be available in the market. You can choose it from the dropdown menu, but for now, you can just change the mcp_config provided by Antigravity and it does the rest. Those configuration options can also be seen here: https://docs.getdbt.com/docs/dbt-ai/about-mcp

This significantly expands what your local agent can do.

And here’s where things get interesting.

You’re no longer just asking for code snippets. You’re enabling deeper project-level awareness and workflows.

MCP servers that pair beautifully

Beyond dbt, there are several MCP servers that elevate the experience:

• GitHub
• Google BigQuery
• AlloyDB
• Dataplex

Now imagine this workflow:

1. A ticket is opened.
2. The agent reads it.
3. It reviews data classification tags in Dataplex.
4. It generates SQL, YAML, and tests.
5. It writes a pull request in GitHub.
6. CI kicks off dbt orchestration and validates everything.

That’s not autocomplete. That’s workflow acceleration.

We’re talking about reducing friction across development, governance, and deployment in one unified environment.

Pro tips for working with Antigravity + dbt

After a few days of experimenting, here are some practical lessons.

1. Pair Antigravity with the Gemini CLI

Use Antigravity for:

• Multi-agent brainstorming
• Large architectural work
• Implementation planning

Use the Gemini CLI for:

• Focused terminal tasks
• Deep maintenance
• Headless execution
• Specific, scoped operations

Together, they create a powerful balance between high-level reasoning and low-level precision.

2. Define rules for your agent

Create global or workspace rules to guide how your agent behaves:

• ~/.gemini/GEMINI.md
• .agent/rule/

You can define:

• Naming conventions
• SQL style standards
• Testing requirements
• Documentation expectations

Think of it as training your agent to be a senior analytics engineer instead of an enthusiastic intern.

3. Add skills for specific tasks

You can extend your agent with specialized skills depending on what you're building. dbt-specific skills are a great place to start: https://docs.getdbt.com/blog/dbt-agent-skills

Skills help tailor the agent’s behavior so it understands how to approach dbt models, testing strategies, documentation, and more.

4. Break large tasks into smaller ones

This one’s critical. Antigravity is very good at:

• Creating implementation plans
• Designing step-by-step execution strategies

It is even better when you:

• Break requests into smaller, precise tasks
• Start at task 1
• Move sequentially

With AI agents, smaller and more specific is almost always better. Think iterative, not monolithic.

Final thoughts

Google entering the agentic IDE space with Antigravity feels like a meaningful shift especially for analytics engineers living in the dbt ecosystem.

Because it’s built on Visual Studio Code, adoption is frictionless.Because it supports MCP servers, it’s extensible.Because it integrates deeply with Gemini, it’s powerful.

And because it can help you write SQL, YAML, tests, PRs, and documentation…it might just give you your weekends back.

No promises. But it’s a strong start.