Google Antigravity is an agentic development platform designed to let AI agents work on software development tasks with a higher degree of autonomy.
Rather than using AI only to generate code snippets, you can give an agent a goal and let it work through multiple development steps. Depending on the setup, it can interact with the code editor, terminal, and browser to build features, investigate problems, and verify parts of the application.
Google introduced Antigravity in November 2025 as an agent-first development platform. Its development environment and related tools have continued to evolve, so available features may differ between versions and interfaces.
Official resources: Google Antigravity documentation and Google's original announcement.
How can Google Antigravity build a website?
Suppose you want to create an e-commerce website for a fashion brand.
You can begin by describing the complete project, including its design, pages, technology stack, and functionality.
For example:
"Build a modern fashion e-commerce website with a premium visual design. Include a homepage, product categories, product detail pages, shopping cart, checkout, customer accounts, and an admin dashboard. Use a responsive layout and organize the code into reusable components. Before making changes, prepare an implementation plan. After development, run the available checks and test the main user journeys in a browser."
The agent can then work through the tasks supported by its available tools.
A typical workflow might include:
Analyzing your requirements.
Preparing a development plan.
Creating the project structure and components.
Implementing the frontend.
Developing backend integrations where required.
Running the application.
Inspecting the interface using browser tools.
Fixing detected issues and checking the results.
The exact level of automation depends on the project, the permissions you grant, and the tools available to the agent.
Why is browser-based testing important?
A website may look excellent in the source code but behave differently when opened in a browser.
Navigation may fail, buttons may not respond, forms may display errors, or mobile layouts may break.
An agent with browser interaction capabilities can help inspect the running website rather than relying exclusively on the code it generated.
For example, you could ask it to:
Open the homepage and check the layout.
Navigate to the services page.
Test whether the contact form validates its inputs.
Check the mobile navigation.
Investigate browser console errors.
Fix layout problems and repeat the relevant checks.
This is a useful part of agentic development, but it does not replace comprehensive testing. A successful browser walkthrough cannot prove that every feature, security control, or edge case works correctly.
What makes Google Antigravity different?
Antigravity is built around an agent-first workflow. Depending on the product surface and version, its capabilities can include working across the editor, terminal, and browser, coordinating tasks, and presenting plans or implementation artifacts for review.
This can be particularly helpful when you want AI to handle several connected development tasks instead of asking it to generate each component individually.
Best suited for: Developers and creators who want to delegate multi-step coding tasks, build prototypes, iterate on interfaces, and use AI agents to help verify their work.
What Are the Limitations of AI Website Builders and Coding Agents?
Despite their capabilities, AI coding tools have several limitations.
1. AI can misunderstand requirements
If you provide incomplete instructions, the agent may implement features differently from what you intended.
Solution: Write clear requirements, review the plan, and work in manageable stages.
2. Generated code can contain bugs
AI-generated code is not automatically correct. A website may look functional while failing under unusual conditions.
Solution: Use tests, inspect changes, and verify important user journeys.
3. Security is not guaranteed
Authentication, file uploads, database permissions, and payment workflows require particular care.
Solution: Follow established security practices and obtain a qualified security review for applications that handle sensitive information or payments.
4. Complex projects require architecture decisions
A large application needs clear boundaries between its frontend, backend, database, and external services.
Solution: Define the architecture early and avoid adding technologies without a clear reason.
5. AI can introduce unnecessary complexity
An agent may install extra packages or create abstractions that make the project harder to maintain.
Solution: Ask it to justify major dependencies and prefer the simplest architecture that meets the requirements.
6. Running the application is not the same as completing it
A successful development server does not prove that the application is secure, accessible, reliable, or production-ready.
Solution: Establish acceptance criteria and test the application before release.
7. AI usage can have costs and limits
Some coding tools have free access options, while others impose subscription, model usage, or API costs. Availability and limits can change.
Solution: Check the current pricing and usage policies of the specific tool before choosing it for a commercial project.
