Introducing Google Antigravity: A New Era in AI-Assisted Software Development

Every significant advancement in coding intelligence models prompts us to reconsider how software development should be approached. The Integrated Development Environment (IDE) of today bears little resemblance to what we used just a few years ago. With the emergence of Gemini 3, Google’s most intelligent model to date, we’re witnessing a fundamental shift in agentic coding capabilities that requires reimagining what the next evolution of development environments should look like.

Today, we’re excited to introduce Google Antigravity, a new agentic development platform that represents a paradigm shift in how developers interact with artificial intelligence. While it includes a familiar AI-powered IDE experience featuring Google’s best models, Antigravity pushes beyond traditional boundaries by evolving toward an agent-first future. This platform combines browser control capabilities, asynchronous interaction patterns, and an agent-centric product design that collectively enable AI agents to autonomously plan and execute complex, end-to-end software development tasks.

Why Google Created Antigravity

The driving vision behind Antigravity is to establish a central hub for software development in the era of intelligent agents. Google’s ultimate goal is to enable anyone with an innovative idea to achieve liftoff—transforming that idea into working reality through assisted development. Starting today, Google Antigravity becomes available in public preview at no cost, with generous usage limits for Gemini 3 Pro.

The development of Antigravity comes at a pivotal moment in AI evolution. With models like Gemini 3, we’ve reached a stage where agentic intelligence can operate for extended periods across multiple surfaces without constant human intervention. While we’re not yet at the point where agents can run for days completely autonomously, we’re moving closer to a world where human-AI interaction occurs at higher levels of abstraction rather than through individual prompts and tool calls. In this emerging landscape, the interface facilitating communication between agents and users needs to look and feel fundamentally different—and Antigravity represents Google’s comprehensive answer to this challenge.

Core Principles of Antigravity

Antigravity represents Google’s first product that integrates four key tenets of collaborative development: trust, autonomy, feedback, and self-improvement. These principles work in concert to create a development environment where humans and AI agents can collaborate effectively.

Building Trust Through Transparency

Trust remains the foundation of effective human-AI collaboration in software development. Most current products occupy two problematic extremes: either overwhelming users with every single action and tool call the agent performs, or showing only the final code changes without any context about how the agent arrived at that solution. Neither approach fosters genuine user confidence in the agent’s work.

Antigravity addresses this trust gap by providing context at a more natural task-level abstraction. The platform presents the necessary and sufficient set of artifacts and verification results that users need to develop confidence in the agent’s work. There’s a deliberate emphasis on ensuring the agent thoroughly considers verification of its work, not just the execution of tasks themselves.

When conversing with an agent in Antigravity, users see tool calls grouped within tasks, alongside high-level summaries and progress indicators. As the agent works, it produces Artifacts—tangible deliverables in formats that users can validate more easily than raw tool calls. These include task lists, implementation plans, walkthroughs, screenshots, and browser recordings. Antigravity agents use these Artifacts to communicate their understanding of the task and demonstrate thorough verification of their work.

Through this interface, users can view the agent’s task list, review implementation plans after research phases but before implementation, or scan walkthroughs upon task completion. This approach creates a verifiable development trail that builds trust through demonstrated competence and transparency.

Enabling Meaningful Autonomy

The most intuitive product design today involves working synchronously with an agent embedded within a specific surface like an editor, browser, or terminal. This is why Antigravity’s primary “Editor view” delivers a state-of-the-art AI-powered IDE experience, complete with tab completions, in-line commands, and a fully functional agent in the side panel.

However, we’re transitioning to an era where agents can operate across multiple surfaces simultaneously and autonomously. With models like Gemini 3, Antigravity agents can perform complex multi-surface workflows without constant supervision.

For example, an Antigravity Agent can autonomously write code for a new frontend feature, use the terminal to launch a localhost server, and control the browser to test whether the new feature works correctly—all without human intervention between steps.

Google believes agents deserve a specialized form factor that optimally exposes this autonomy while allowing for more asynchronous user interaction. In addition to the IDE-like Editor surface, Antigravity introduces an agent-first Manager surface that reverses the traditional paradigm. Instead of embedding agents within surfaces, this approach embeds surfaces into the agent. Think of it as mission control for spawning, orchestrating, and observing multiple agents across parallel workspaces.

This design enables scenarios where a user might spawn an agent to conduct background research in a separate workspace while focusing on a more complex task in the foreground. The user can monitor progress through the Inbox and side panel in the Agent Manager, receiving notifications when milestones are reached or input is required.

Rather than attempting to squeeze both asynchronous Manager experiences and synchronous Editor experiences into a single window, Antigravity optimizes for instantaneous handoffs between these modes. This forward-looking design intuitively brings software development into the asynchronous era, anticipating continued rapid improvements in model intelligence like Gemini.

Facilitating Effective Feedback

A critical limitation of remote-only development solutions is the difficulty of iterating easily with AI agents. While agentic intelligence has improved significantly, it remains imperfect. An agent that completes 80% of a task should provide substantial value, but if providing feedback on the remaining 20% requires disproportionate effort, the net benefit diminishes considerably.

User feedback mechanisms allow us to avoid treating agents as binary systems that are either perfect or useless. Antigravity begins with local operation, enabling intuitive asynchronous user feedback across every surface and Artifact. This includes Google-docs-style comments on text Artifacts and select-and-comment feedback on visual elements like screenshots. Crucially, this feedback automatically incorporates into the agent’s execution without requiring users to stop the agent’s process.

The platform provides examples of feedback on textual Artifacts like implementation plans, as well as visual Artifacts like screenshots captured by the Agent. This seamless feedback integration creates a collaborative development loop where human intuition and machine execution complement each other effectively.

Supporting Continuous Self-Improvement

Antigravity treats learning as a core primitive, with agent actions both retrieving from and contributing to a shared knowledge base. This knowledge management system enables agents to learn from past work, capturing both explicit information like useful code snippets or architectural patterns, and more abstract knowledge like successful sequences of steps for particular subtasks.

Through this mechanism, agents learn from both completed work and user feedback to generate and leverage knowledge items, all viewable from the Agent Manager. This creates a virtuous cycle where the system becomes more effective with each project it completes or feedback iteration it processes.

Getting Started with Antigravity

Google believes Antigravity’s product design represents the next fundamental advancement in agent-assisted development. The company’s goal is to refine it into the best possible product offering for end users. The public preview available today includes:

• Google Antigravity for individual users at no charge
• Compatibility with MacOS, Linux, and Windows operating systems
• Access to Google’s Gemini 3, Anthropic’s Claude Sonnet 4.5 models, and OpenAI’s GPT-OSS within the agent, providing developers with model optionality

Installation Process

1. System Requirements Check: Ensure your development machine meets the minimum requirements for your operating system. Antigravity supports recent versions of MacOS, Linux, and Windows.

2. Download the Application: Visit the official Antigravity website to download the appropriate version for your operating system.

3. Installation Steps:

   • For Windows: Run the installer executable and follow the setup wizard
   • For MacOS: Mount the DMG file and drag Antigravity to your Applications folder
   • For Linux: Use the provided package or the manual installation script

4. Initial Setup: Upon first launch, you’ll be guided through account creation and basic configuration, including model preference selection.

5. Workspace Configuration: Choose your default workspace settings or import existing development environment configurations.

First-Time User Guide

For developers new to agentic development environments, we recommend this onboarding sequence:

1. Familiarization with Interfaces:

   • Spend time exploring both the Editor and Manager views
   • Practice switching between synchronous and asynchronous interaction modes
   • Learn the different types of Artifacts agents can produce

2. Initial Simple Project:

   • Start with a well-defined, modest coding task
   • Observe how the agent breaks down the problem and produces intermediate Artifacts
   • Practice providing feedback at different stages of the process

3. Multi-Agent Experimentation:

   • Once comfortable with single-agent workflows, try spawning multiple agents for different tasks
   • Use the Manager view to monitor progress across workspaces
   • Practice using the Inbox for agent notifications and communications

4. Knowledge Base Exploration:

   • Examine how the system captures and retrieves knowledge from completed work
   • Contribute explicitly to the knowledge base through manual entries
   • Observe how past solutions influence current agent behavior

Practical Use Cases

Antigravity excels across various development scenarios:

Feature Development: From concept to implementation, agents can handle research, coding, testing, and documentation for new features.

Code Migration: Assist with porting code between frameworks or versions, with the agent handling repetitive pattern changes while you focus on architectural decisions.

Debugging Complex Issues: Agents can systematically investigate problems across codebases, browsers, and logs to identify root causes.

Research and Prototyping: Spawn agents to explore new technologies or create proof-of-concept implementations while you focus on primary tasks.

Documentation Generation: From code comments to user manuals, agents can produce and maintain documentation aligned with code changes.

Frequently Asked Questions

How does Antigravity differ from existing AI coding assistants?

While traditional AI coding assistants primarily focus on code completion and generation within editors, Antigravity operates at a higher level of abstraction. It handles complete tasks across multiple surfaces (editor, browser, terminal) and produces verifiable Artifacts beyond just code. The platform’s agent-first design, asynchronous capabilities, and knowledge retention system create a more comprehensive development partnership rather than just a coding tool.

What makes Antigravity suitable for team development environments?

Although the current preview focuses on individual developers, Antigravity’s architecture supports team development through its knowledge management system, artifact sharing, and verification workflows. The platform’s emphasis on transparent processes and verifiable outputs makes it easier for team members to understand and build upon each other’s work, including work performed by agents.

How does the model selection work within Antigravity?

Antigravity provides access to multiple state-of-the-art models including Google’s Gemini 3, Anthropic’s Claude Sonnet 4.5, and OpenAI’s GPT-OSS. Developers can configure default model preferences for different types of tasks, and the system may automatically select or recommend models based on task requirements and historical performance patterns.

Can Antigravity integrate with existing development tools and workflows?

Yes, Antigravity is designed to complement rather than replace existing development tools. The platform integrates with standard version control systems, project management tools, and continuous integration pipelines. Its browser control capabilities allow it to work with web-based development tools and documentation resources.

What types of verification does Antigravity perform on its work?

Antigravity agents employ multiple verification strategies depending on the task type. These can include code validation, test execution, visual verification through screenshots and browser recordings, consistency checks against implementation plans, and user feedback incorporation. The verification process is documented through Artifacts that users can review.

How does the knowledge management system handle organization-specific information?

The knowledge base can capture both general technical patterns and organization-specific implementation approaches. As teams use Antigravity across projects, the system builds an increasingly valuable repository of proven solutions, preferred patterns, and domain-specific knowledge that improves agent performance over time.

What safeguards prevent knowledge base contamination from incorrect solutions?

Antigravity implements multiple safeguards including user feedback integration, solution success tracking, and periodic knowledge validation. Users can explicitly flag incorrect or suboptimal solutions, and the system weights knowledge items based on verification results and usage patterns.

The Future of Agentic Development

Antigravity represents Google’s vision for the next evolution of AI-assisted software development. By addressing the fundamental pillars of trust, autonomy, feedback, and self-improvement, the platform creates an environment where developers and AI agents can collaborate more effectively than ever before.

The transition from tools that execute discrete commands to partners that understand and execute complex tasks represents a qualitative shift in how we approach software creation. As model capabilities continue to advance, platforms like Antigravity will enable developers to operate at increasingly higher levels of abstraction, focusing more on architectural decisions and creative problem-solving while delegating implementation details to capable AI agents.

For developers interested in exploring this new frontier of development methodology, Antigravity’s public preview provides an accessible entry point. The free tier with generous usage limits makes it possible to thoroughly evaluate the platform’s capabilities without financial commitment.

To learn more about Antigravity’s specific features, explore the comprehensive documentation available through the platform’s official website. Additional use cases and implementation examples provide practical guidance for integrating agentic development into various workflows. Regular updates through the official blog and social media channels ensure users stay informed about new features and improvements as the platform evolves.

The countdown to a new era of software development has reached its final seconds. Experience liftoff in 3… 2… 1…