Google Antigravity Explained: The New Way to Build Apps With Vibe Coding (2026)

There’s a specific kind of YouTube video I kept running into lately. The thumbnail has someone pointing dramatically at a screen with a title like “I Built an Entire App in 3 Hours With This Tool” or “Build ANYTHING With Google Antigravity.” They show off sleek dashboards, animated interfaces, and full SaaS products built in a blink.

It, honestly, left me more curious than skeptical.

At the time, I was using Cursor as my main AI-assisted IDE (Integrated Development Environment), and it was doing the job just fine. But the videos kept coming up, and eventually, I just had to see what the hype was about.

So, I did what any curious dev would do: I gave Antigravity a try on a real project—VersoID, the digital business card app I’m building in Flutter. It’s a real work-in-progress that I actually care about, which is the best way to test any tool, right? 💁‍♀️

And here we are.

I’m not going to tell you Antigravity is the definitive winner over Cursor or any other tool. I don’t think we’re at a point where that verdict is clear-cut.

But I can show you:

What Antigravity actually does differently.
What the learning curve looks like for us humans.
How to structure your workflow so you’re not just “vibing” your way into a broken codebase.

Note: If you’re a non-technical founder who’s never written a line of code, this guide is especially for you. You don’t need a CS degree to follow along; you just need a project idea and a willingness to be the one steering the ship.

What Even Is “Vibe Coding”?

Before we open Antigravity, let’s clear up “vibe coding” because it gets thrown around a lot, usually without much context.

Vibe coding describes a shift in how you interact with code.

Instead of writing every line yourself, you describe what you want in plain language, and an AI figures out how to build it.

You’re the director, and the AI is doing the implementation work.

The term “vibe coding” was coined by AI researcher Andrej Karpathy in early 2025. Since then, it’s become shorthand for this whole category of natural-language-driven development.

What does that actually look like in practice?

Instead of hunting for the syntax to create a button that looks like this:

const button = document.createElement('button')

You type something like:

“Add a sign-up button to the home screen that routes to the registration page and matches the existing color scheme.”

The AI reads that, writes the code, and you review the result.

Now, full vibe coding, where you trust the AI completely and never really look at what it produces, is great for fast prototypes or throwaway demos.

But if you’re building something you actually intend to ship to real users? You need more structure.

Which is exactly why tools like Antigravity are designed around systems, not just prompts.

Meet Antigravity: More Than a Smarter Code Editor

If you’ve ever used VS Code, the Antigravity editor is going to immediately feel familiar. The layout is almost identical—file tree on the left, editor in the center.

This was a huge relief for me because the learning curve for the interface itself is basically zero. You aren’t relearning how to navigate a workspace, which matters more than you’d think when you’re trying to move fast!

If you’ve never used a code editor before at all, think of this as a place where your project files live and where you can see and edit the code your agent writes. You won’t be spending much time manually editing files, but it’s good to know it’s there.

However, the interface familiarity is kind of where the similarity ends, because what Antigravity is actually doing under the hood is different.

Rather than an assistant that autocompletes lines or suggests fixes, Antigravity is built around autonomous agents. These are AI workers you can assign tasks to, run in parallel, and review before their changes hit your codebase.

Think of it less like a smart editor and more like a project management layer sitting on top of your code.

You’ll bounce between two main views:

The Editor: Your files, code, and terminal. For most, a familiar ground.
The Agent Manager: This is where you spin up agents, give them tasks, track what they’re doing, and approve or reject their proposed changes before anything gets committed.

That last part matters more than it sounds. You’re not just prompting and hoping—you’re reviewing. You’re actively engaging with the work to be done.

Note ⚠️
You’ll need a personal Gmail account to sign in since Workspace accounts aren’t supported in the current preview.

The Three-Layer Architecture (Your agents.md Blueprint)

Before we start building, we need a rulebook. In Antigravity, this is a file called agents.md. Without this file, your agent is working blind. It has no idea what your “vibe” is or what your tech stack requires.

But agents.md isn’t just a description of your project. It needs to be organized in three distinct layers, and this structure is what separates a vague prompt from a build that stays consistent.

To help you visualize, think of it like running a restaurant: you need a menu, kitchen rules, and the actual cooking.

We’ll take a look at each of the three layers. I’ll be referencing my current in-progress app for the examples.

Explore: All You Need To Know About AI Workflow Files And How To Use Them

Layer 1: The Directive (What to build)

The first layer is your high-level goal along with the inputs, outputs, and required tools. This answers “what are we doing and why?”

## Directive
Build a digital business card app.
Input: user fills in profile info, links, and uploads a photo.
Output: shareable profile page with a unique URL and QR code.
Required: Flutter, Firebase auth, QR code package.

Layer 2: Orchestration (How to decide)

This is where you define the agent’s judgment rules. What gets prioritized? What requires your approval before proceeding?

## Orchestration
- Mobile experience first, always
- If a package isn't available for Flutter, flag it before trying alternatives
- Ask for review before any schema or auth changes
- Use planning mode for anything involving user data

Layer 3: Execution (Where the work lives)

Lastly, the boring (but vital) structural stuff. This includes your folder conventions, where sensitive data goes, and any standards for the code itself.

## Execution
- Source files in /lib (Flutter convention)
- All API keys in .env — never in source
- Follow Flutter widget composition patterns

When all three layers are in place, the agent has enough context to work semi-autonomously.

Without them, you get an agent that produces inconsistent results across sessions and “fixes” things in ways you didn’t ask for.

Note 😬
I once skipped the Execution layer and the agent decided to “optimize” my folder structure by moving everything into one giant file. It was a nightmare to untangle and was burning through tokens by having the AI read an extremely long file. Learn from my mistake: give the agent clear boundaries!

The SBDT Framework: Your Map Through the Build

Most people make the mistake of opening Antigravity and typing: “Build me an app like Uber.” Then they wonder why the output is a messy disaster.

The tool isn’t the problem, nor is it broken. The AI isn’t “stupid” 🙄

The problem here is the missing structure. To get real results, I follow the SBDT Framework:

S — Structure

Before your agent writes a single line of code, you need to create a workspace. This is a dedicated folder on your computer where everything lives.

Everything starts here, and if you shortcut this step, you’ll feel it later.

After creating a workspace, you give your agent context about what you’re building through that agents.md file we just built out.

Tip 💡
Think of the Structure phase as the moment you hand your agent its instructions before the real work begins.

Here’s a simplified version of what one might look like:

# Project: VersoID

## Goal
A digital-first multi-purpose business card app built in Flutter.
Users create a shareable profile with links, contact info, and media.

## Tech Stack
- Flutter (mobile)
- Firebase (auth + database)
- AI: Gemini for UI, Claude for logic

## Rules
- API keys and credentials in .env only — never hardcoded
- Mobile-first, always
- Ask for review before any database schema changes

Then go to the Agent Manager and kick things off:

@agents.md — initialize the project structure

The agent will propose a folder layout. Review it, click “Accept”, and you’ve got your foundation.

Tip 👀
The more specific your agents.md, the less back-and-forth you’ll have later. However, and this is really important: over-direct and you risk getting sub-optimal solutions.

B — Build

With the structure in place, the agent creates an Implementation Plan before touching a single file. It’s usually a markdown file that says: “Here’s exactly what I’m going to do, which files I’m touching, and why.”

Tip: Another way you can get a plan of the work to be done is by selecting “planning mode”. Terminologies and functionalities change with updates so make sure to search how to get a plan in your current version.

The plan offers you a chance to redirect. If it looks off, tell the agent to change it before it starts writing code.

This is also where you make a key decision: which AI model is doing the work?

I’ve been building VersoID primarily with Gemini as my co-coding partner, and there’s a reason for that.

In my experience, Claude tends to over-engineer solutions. It’ll solve your problem, but it might also restructure three other things you didn’t ask it to touch, or propose an abstraction layer that’s technically elegant but overkill.

I find Gemini to be more of a worker bee who does what you ask very well. It’s also more accessible from a cost standpoint if you’re building on a budget.

That’s not a knock on Claude, since for complex back-end logic, it can be really strong. But for day-to-day building? Gemini gets the most work done without you spending a dime.

More: The Ultimate Guide To Re-Engineering My Portfolio’s RAG Chatbot

T — Test

This is where Antigravity does something most tools don’t. If you’re building anything with a frontend, you can spin up a Browser Sub-Agent—a specialized agent that opens a real Chrome window and actually tests the UI.

The Browser Sub-Agent clicks buttons, fills out forms, and captures screenshots and video recordings of everything it does.

If a button throws an error, the agent detects it, checks the agents.md for context, and self-heals the code automatically. Here’s what that actually looks like 👇

Say your agent builds a registration form, but the submit button throws an error because it’s referencing a function that doesn’t exist yet.

Instead of stopping and waiting for you, Antigravity:

Detects the broken reference
Checks your agents.md directive for context about what the form was supposed to do
Patches the missing logic automatically

My favorite part: It logs what it caught and what it changed 🤓

Now, I’m not saying it’ll fix architectural mistakes or decisions that conflict with your original brief. But it’s great for the routine stuff that breaks during a build!

Note 📍
Self-healing works best when your agents.md is detailed. If the context is vague, the agent might “fix” something in a way that technically runs but isn’t what you intended. The Structure phase is what makes this reliable.

D — Deployment

Antigravity builds the app, but it doesn’t host it.

When you’re ready to “go live”, you’ll still need tools like GitHub (for version control) and something like Vercel or Firebase (for deployment).

Your agent can help you write the config files, but you’re the one who has to ultimately “publish” the app.

If you’re brand new to deployment, set aside some time to learn the basics separately. It’s not hard (positive understatement 😅), but walking in unprepared will slow you down.

But Wait, It’s Not Just for Coders

Remember those YouTube videos from the intro? The ones showing people building dashboards, animated sites, and entire apps?

A lot of them also showed something I didn’t expect: content workflows.

Not a single line of code in sight. And that’s because Antigravity has a feature called Skills. These are local files that teach your agent how to do repeatable tasks, like writing LinkedIn posts in your voice or doing SEO research.

I haven’t used Skills myself in the context of content generation yet, but here’s what I’ve seen and what the documentation confirms: Skills stay on your local machine. They don’t burn through your AI token quota every time you run them because the instructions are read locally.

What people are building:

LinkedIn drafter skill that researches a topic and writes posts in a specific voice or style.
Brand guidelines skill that keeps every output consistent with your tone, colors, and messaging (similar to my design system use-case).
SEO consultant skill that runs your content through a set of optimization rules before publishing.
Social media scheduler skill that formats content for different platforms automatically.

Whether you’re building with widgets or words, the workflow is the same!

A Few Things Worth Knowing

Antigravity may sound like a great tool—it is. It’s free, provides an agentic workflow orchestration UI, and can be used by tech and non-tech users.

However, there are still a few nuances worth sharing:

Startup Bugs: Early on, I had issues where the app would just freeze on launch. It seems patched now, but remember that this is relatively new tech. Expect some “rough-around-the-edges” moments.
Rate Limits: “Free” has a limit. If you run five agents at once, you’re going to hit a wall fast. So, pace yourself!
Security: Don’t just copy-paste agents.md files from the internet. Malicious files can be “prompted” to steal your API keys or credentials. Call me paranoid, but it’s better to be safe than sorry🛡️
Deployment: Though you can get shockingly far without touching the terminal, pushing a real app live still requires knowing Git and at least one hosting platform.

Your First Session: Quick-Start Checklist

Ready to give Antigravity a go? Here’s the sequence I’d follow:

Download & Sign-in: Use a personal Gmail.
Create a Workspace: Just a folder on your desktop will do.
Write your agents.md: Use the Directive, Orchestration, Execution layers.
Initialize: Use @agents.md in the Agent Manager.
Start Small: Don’t try to build the whole app at once. Start with “Build a login screen.”
Review the Plan: Don’t just hit “Accept” without reading what the agent intends to do!

Note 📝
“Build the user profile screen” will get you further than “Build me the whole app.” Scope it small, ship something real, then expand from there. Incrementally test and clear bugs as you go!

What I’m Still Figuring Out

I’ll be honest with you: I’m still not 100% sure why I’d pick Antigravity over Cursor for every project. I have preferences—I love the Agent Manager and the way Gemini handles the build loop—but I haven’t landed on a “definitive” reason yet.

What I do know is that the workflow I described in this post produces more consistent results than just prompting a chat window and pasting code.

Whether that’s unique to Antigravity or a systems problem that any tool would solve with the right setup, that’s still a question I’m pondering 🤔

If you’ve made the switch from Cursor (or something else) and you have a stronger take, I’d love to hear it in the comments!

It’s a Wrap

To sum: Vibe coding isn’t about removing yourself from the process. It’s about shifting where your attention goes.

Though you aren’t writing every line, you’re writing the rules, reviewing the plan, and steering the agent.

The structure you put in upfront is what determines whether the output is useful or chaotic.

Antigravity makes that loop surprisingly smooth once you’ve got your agents.md added in with system architecture directions. Whether it’s your go-to tool is a question only your actual projects can answer.

Go try it on something real. That’s the only way to know!

Thanks for spending time on this one.

See ya 👋

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