The Ultimate Guide to Using Google Antigravity Code Editor

Artificial intelligence is rapidly changing how developers build software. Over the last few years, tools like AI coding assistants have helped developers autocomplete code, generate functions, and debug problems faster than ever before.

But a new category of developer tools is starting to appear: agent-first development environments. Instead of simply suggesting code while you type, these tools allow AI agents to take on much larger tasks. They can plan work, write code, run commands, test applications, and report the results back to you.

This shift has also led to the rise of a new development style often referred to as "vibe coding", where developers describe the outcome they want, and AI systems handle most of the implementation.

(If you're unfamiliar with vibe coding, I wrote a detailed guide explaining how it works and why it's gaining popularity among developers.)

Code Less, Build More: The Vibe Coding Shift

Vibe coding—using AI to generate code from natural language descriptions—is transforming how developers work. This post explores the productivity gains, hidden risks, and why human judgment remains irreplaceable in building reliable software.

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One of the most interesting tools in this space is Google Antigravity, an experimental AI-powered IDE designed around autonomous coding agents rather than simple code suggestions. In this guide we'll explore:

• What Google Antigravity is
• How it works
• The key concepts behind the platform
• Important features like Rules, Workflows, Skills, and Task Groups
• A typical development workflow with Antigravity
• Why tools like this could reshape how developers build software

What is Google Antigravity Code Editor?

Google Antigravity is an AI‑powered development environment designed around autonomous agents. Instead of only helping you write code faster, Antigravity allows developers to delegate development tasks directly to AI agents.

These agents can perform actions such as:

• Writing code
• Running terminal commands
• Launching browsers
• Testing applications
• Debugging errors
• Documenting their work

In other words, the AI is not just assisting you, it acts more like a collaborator inside your development environment.

Antigravity is powered primarily by Google's Gemini models, but it can also work with other models depending on configuration. The goal is to combine powerful language models with developer tools so that agents can automate large parts of the development workflow.

Google Antigravity

Google Antigravity - Build the new way

antigravity.google

The Rise of Agent-First Development

Traditional AI coding tools operate within a familiar workflow.

You type code, and the AI suggests completions.

For example:

Developer writes code → AI suggests improvements

But Antigravity flips this model entirely. Instead, developers describe the goal, and the AI agent handles the implementation.

Example workflow:

Developer defines task → AI plans solution → AI writes code → AI tests result

This approach is known as agent-first development. Instead of assisting developers, AI agents become autonomous problem solvers capable of executing complex workflows.

These agents can work across multiple environments simultaneously:

• Code editor
• Terminal
• Browser

This allows them to build and validate entire applications from a single instruction.

Core Architecture of Google Antigravity

Antigravity is built around several key components that enable autonomous development workflows.

1. Autonomous AI Agents

The heart of Antigravity is its AI agent system.

These agents are capable of performing multi-step development tasks independently.

Agents can:

• Generate code
• Install dependencies
• Execute terminal commands
• Run applications
• Test user interfaces
• Debug runtime issues

Unlike simple code generation tools, Antigravity agents operate across multiple environments simultaneously including the editor, terminal, and browser.

This enables true end-to-end automation.

For example, an agent could:

• Create a new project
• Install dependencies
• Write application logic
• Launch a development server
• Open a browser to test the UI

All without manual intervention.

2. Artifacts System

One major challenge with autonomous AI systems is trust. To address this, Antigravity introduces a system called Artifacts.

Artifacts are structured outputs that document the work performed by an AI agent.

Artifacts can include:

• Task lists
• Implementation plans
• Code diffs
• Test results
• Screenshots
• Browser recordings

These artifacts allow developers to verify the AI's work before accepting changes.

Rather than blindly trusting AI-generated code, developers can review transparent evidence of the agent's actions.

3. Agent Manager

Antigravity includes a powerful control interface known as the Agent Manager. This feature acts like a mission control center for managing AI agents.

With Agent Manager, developers can:

• Launch multiple agents
• Assign tasks
• Monitor progress
• Review artifacts
• approve or reject changes

Multiple agents can also work in parallel across different projects, dramatically increasing development speed.

This introduces a new development paradigm sometimes called AI swarm development.

4. The Editor

Although Antigravity introduces new AI capabilities, it still retains the familiar interface of traditional IDEs.

The platform includes an editor similar to Visual Studio Code.

Features include:

• Syntax highlighting
• File explorer
• extension ecosystem
• tab completion
• AI chat panel

This ensures developers can continue using familiar workflows while gradually adopting AI automation.

Key Concepts in Google Antigravity

To fully understand how Google Antigravity boosts developer productivity, it's important to understand some of the platform's core concepts. These include models, agent modes, skills, workflows, browser subagents, sandboxing, and MCP integration.

Each of these components plays a role in enabling Antigravity's agent-first development approach.

Models

In Antigravity, models refer to the underlying AI systems that power the agents.

These models provide the reasoning and coding capabilities needed to complete tasks.

Common supported models include:

• Gemini
• Claude
• GPT

In Antigravity, developers can switch models depending on the complexity of the task. This flexibility allows teams to balance cost, speed, and accuracy.

Agent Modes & Settings

Antigravity agents can operate in different modes, which control how much autonomy the agent has.

Typical modes include:

Planning Mode

The agent only creates a plan and does not execute actions. This is useful when you want to review the approach before code is generated.

Execution Mode

The agent can:

• write code
• run terminal commands
• modify files

This mode is used for active development tasks.

Autonomous Mode

The agent performs the entire workflow:

• planning
• coding
• testing
• debugging

This is the most powerful mode and allows agents to operate almost like a junior developer.

Rules

Rules define persistent behavioral guidelines that the AI agent must always follow. Think of them as guardrails for your AI developer.

For example, you might enforce:

• coding standards
• architecture decisions
• security policies

Rules are always active during interactions.

Creating a rule

To get started with Rules:

• Open the Customizations panel via the "…" dropdown at the top of the editor's agent panel.
• Navigate to the Rules panel.
• Click + Global to create new Global Rules, or + Workspace to create new Workspace-specific rules.

A Rule itself is simply a Markdown file, where you can input the constraints to guide the Agent to your tasks, stack, and style.

Rules files are limited to 12,000 characters each.

Rule Hierarchy

Antigravity rules exist at multiple levels.

System Rules: Built-in rules that define core AI safety and behavior.

Global Rules:  This rule applies across all projects. Global rules live in 

~/.gemini/GEMINI.md

and are applied across all workspaces.

Workspace Rules:

Workspace rules live in the .agent/rules folder of your workspace or git root.

At the rule level you can define how a rule should be activated:

Manual: The rule is manually activated via at mention in Agent's input box.

Always On: The rule is always applied.

Model Decision: Based on a natural language description of the rule, the model decides whether to apply the rule.

Glob: Based on the glob pattern you define (e.g., .js, src/**/.ts), the rule will be applied to all files that match the pattern.

Workflows

Workflows enable you to define a series of steps to guide the Agent through a repetitive set of tasks, such as deploying a service or responding to PR comments. These Workflows are saved as markdown files, allowing you to have an easy repeatable way to run key processes. Once saved, Workflows can be invoked in Agent via a slash command with the format /workflow-name.

Creating a workflow

To create a workflow:

• Open the Customizations panel via the "…" dropdown at the top of the editor's agent panel.
• Navigate to the Workflows panel.
• Click the + Global button to create a new global workflow that can be accessed across all your workspaces, or click the + Workspace button to create a workflow specific to your current workspace.

Workflow files are limited to 12,000 characters each.

Workflow Structure

Workspace workflows are stored in .agent/workflows folder of your workspace or git root.

E.g:

.agent/workflows/create_component.md

---
description: Create a React component with standard structure
---
 
1. Ask the user for the component name.
 
2. Create the following files:
 
components/[ComponentName]/
  - index.ts
  - [ComponentName].tsx
  - [ComponentName].test.tsx
 
3. Generate component boilerplate code.
 
4. Generate unit tests.
 
5. Export the component in index.ts
Execution
To execute a workflow, simply invoke it in Agent using the /workflow-name command. You can call other Workflows from within a workflow! For example, /workflow-1 can include instructions like "Call /workflow-2" and "Call /workflow-3". Upon invocation, Agent sequentially processes each step defined in the workflow, performing actions or generating responses as specified.

Skills

Skills are reusable capabilities the AI agent can invoke automatically.

Unlike workflows, users don't manually trigger skills. Instead, the agent decides when to use them based on the task.

Each skill contains:

• Instructions for how to approach a specific type of task
• Best practices and conventions to follow
• Optional scripts and resources the agent can use

When you start a conversation, the agent sees a list of available skills with their names and descriptions. If a skill looks relevant to your task, the agent reads the full instructions and follows them.

Skill Directory Structure

Workspace: <workspace-root>/.agent/skills/<skill-folder>/

Global: ~/.gemini/antigravity/skills/<skill-folder>/

Folder Structure:

While skill.md is the only file you need inside your skills folder, you can add additional resources

.agent/skills/my-skill/
├─── SKILL.md       # Main instructions (required)
├─── scripts/       # Helper scripts (optional)
├─── examples/      # Reference implementations (optional)
└─── resources/     # Templates and other assets (optional)

Creating and executing a skill

To create a skill:

• Create a folder for your skill in one of the skill directories
• Add a SKILL.md file inside that folder

.agent/skills/
└─── my-skill/
    └─── SKILL.md

Skill format

---
name: my-skill
description: Helps with a specific task. Use when you need to do X or Y.
---
 
# My Skill
 
Detailed instructions for the agent go here.
 
## When to use this skill
 
- Use this when...
- This is helpful for...
 
## How to use it
 
Step-by-step guidance, conventions, and patterns the agent should follow.
Example:
---
name: create-api-endpoint
description: Generates a REST API endpoint with validation, controller logic, and unit tests. Use when creating new backend endpoints.
---
 
# Create API Endpoint Skill
 
This skill helps generate a complete REST API endpoint following project conventions.  
It creates the controller, route registration, validation logic, and unit tests.
 
The goal is to ensure all API endpoints are implemented consistently across the project.
 
---
 
## When to use this skill
 
Use this skill when:
 
- Creating a new backend API endpoint
- Adding a new feature that requires server-side data access
- Implementing CRUD operations
- Building microservice endpoints
 
This is helpful because it ensures:
 
- consistent API structure
- built-in validation
- automatic test generation
- standardized routing
 
---
 
## How to use it
 
Follow these steps when generating a new endpoint.
 
### 1. Determine Endpoint Details
 
Ask the user for:
 
- endpoint name
- HTTP method (GET, POST, PUT, DELETE)
- request parameters
- response structure
 
Example:
 
Example:
 
```ts
import { Request, Response } from "express";
 
export async function createUser(req: Request, res: Response) {
  try {
    const { name, email } = req.body;
 
    if (!name || !email) {
      return res.status(400).json({
        error: "Name and email are required"
      });
    }
 
    const user = {
      id: Date.now(),
      name,
      email
    };
 
    return res.status(201).json(user);
  } catch (error) {
    return res.status(500).json({
      error: "Internal server error"
    });
  }
}

Task Groups

Task Groups organize multiple tasks into structured work units.

For example, a feature like user authentication might include several tasks:

• create database schema
• build login UI
• implement API endpoints
• add tests

Instead of running each task individually, Antigravity allows them to be grouped together. This makes it easier to manage large development workflows across multiple agents.

Strict Mode

Strict mode is a safety feature that limits what agents are allowed to do.

When strict mode is enabled:

• agents cannot run destructive commands
• file system access may be restricted
• certain operations require approval

This helps prevent mistakes when running autonomous AI agents.

Strict mode is particularly useful when working on production environments or critical projects.

Sandboxing

Sandboxing isolates the environment where agents operate.

Instead of executing commands directly on your system, the agent runs tasks inside a controlled environment.

Benefits of sandboxing include:

• improved security
• protection from destructive commands
• reproducible development environments

If something goes wrong, the sandbox can simply be reset without affecting your main system.

MCP (Model Context Protocol)

Another important concept in Antigravity is MCP, short for Model Context Protocol.

MCP is a standardized way for AI models to connect with external tools, services, and data sources.

Think of MCP as a bridge between AI agents and the outside world.

Through MCP integrations, agents can interact with tools such as:

• databases
• APIs
• development platforms
• cloud services

This allows Antigravity agents to operate within real development ecosystems rather than being limited to code generation.

For example, an MCP integration could allow an agent to:

• fetch data from a database
• deploy an application to the cloud
• interact with project management tools

This greatly expands what AI agents can accomplish.

How Google Antigravity Works

To understand Antigravity, it helps to look at a typical development workflow.

Step 1: Install the IDE

Antigravity is installed locally on your machine and currently supports:

• macOS
• Windows
• Linux

Users also need:

• a Google account
• Chrome browser for browser automation.

Step 2: Launch Agent Manager

After installation, the primary interface you'll see is Agent Manager.

This is where developers initiate conversations with AI agents and assign tasks.

Each task becomes a conversation thread that tracks:

• prompts
• artifacts
• progress updates

Step 3: Assign a Task to an Agent

You can instruct the agent using natural language.

Example prompt:

Create a Next.js dashboard with Tailwind UI and analytics charts.

The agent will then:

• Analyze the request
• Create a task plan
• Generate code
• Launch the application
• Run tests
• provide results

Step 4: Review Artifacts

As the agent completes work, it generates artifacts such as:

• implementation plans
• screenshots
• browser recordings
• logs

Developers can review these artifacts and provide feedback before approving changes.

Example: Building an App with Antigravity

Imagine you want to create a small web app.

Instead of coding everything manually, you might give this prompt:

Build a Flask web application for a one-day tech conference with speaker schedule and sessions.

The agent may then:

• Generate the Flask project
• Create HTML templates
• add CSS styling
• start the development server
• test the UI
• generate screenshots

Developers can view the results directly inside the IDE.

This drastically reduces development time.

Key Features of Google Antigravity

1. Autonomous Development

AI agents can independently execute complex coding workflows.

2. Multi-Model Support

Antigravity supports multiple AI models including:

• Gemini 3 Pro
• Claude Sonnet
• OpenAI-compatible models.

3. Browser Automation

Agents can interact with web browsers to test applications.

They can:

• click buttons
• fill forms
• capture screenshots
• record sessions

4. Knowledge System

Antigravity includes a knowledge system that stores reusable patterns and workflow knowledge.

Over time, agents become more efficient by learning from previous tasks.

5. Parallel Development

Multiple agents can work on different tasks simultaneously.

For example:

Agent 1 → frontend UI

Agent 2 → backend API

gent 3 → automated tests

Challenges and Risks

Despite its potential, agent-driven development introduces new risks. AI agents with system-level access can potentially run harmful commands if misinterpreting instructions.

As a result, developers must carefully review artifacts and maintain proper safeguards.Security and oversight remain critical when using autonomous AI tools.

The Future of AI-Driven Development

Tools like Antigravity suggest that the role of developers may evolve significantly. Instead of writing every line of code, developers may increasingly act as:

• system architects
• AI orchestrators
• reviewers of generated code

Software development may become more about directing intelligent agents than manual programming.

Ready to take your coding to the next level? Open Google Antigravity and start your first project today!