🕸️ 7 Google Antigravity Features that Vibe Coders Use to Build 10X Faster

I. Introduction: It's Not a Chatbot, It's an Orchestrator

The first time I used Antigravity correctly, I built a real app in 10 minutes, not by coding faster but by managing AI agents like a tech lead.

While the tech world obsesses over Gemini 3.0’s raw power, it’s overlooking the real revolution: Antigravity isn’t a chatbot.

I stopped asking it to “write code” and started treating it like a team of engineers I manage. But most people I see are still using it the wrong way, like ChatGPT with a code editor attached.

In this guide, I will break down the 7 important features that separate the "vibe coders" who ship fast from the developers still stuck in the old way of working.

*Improtant Note: If this is your first time hearing about Antigravity, take a few minutes to read this post and watch this video. It will help you understand how these features work and make everything else much clearer.

II. Feature #1: Agent Manager - Your Mission Control Dashboard

Most AI coding tools still work like chatbots. You ask, wait and hope the output makes sense, it’s boring. This feature #1 changes that completely. This is where AI stops acting like a black box and starts behaving like a team you can actually manage.

1. The Problem Everyone's Ignoring

When you use a standard AI coding tool like Claude Code or GitHub Copilot, you're working with a black box. You prompt the AI, it does something behind the scenes and you get an output. If it fails or goes off-track, you have zero visibility into why or where it derailed. This is the blind leading the blind.

2. The Antigravity Solution: The Inbox System

Antigravity flips this model entirely with the Agent Manager, a mission control dashboard that treats agents as asynchronous workers you can spawn, monitor and redirect in real-time.

Instead of a linear chat interface where messages stack on top of each other, Antigravity uses an inbox-based system. Each agent gets its own thread. You can click into any agent at any time to see:

• Its thought process (the reasoning behind its decisions).

• Its execution plan (the step-by-step tasks it's planning).

• Its real-time activity log (watching it browse documentation, write code and run tests).

3. Real-World Demonstration: Building a Market Intelligence Agent

If you want to try Antigravity but don’t know how to start. This one is for you. Here is how I used the Agent Manager to build a multi-component app simultaneously.

Below are 3 prompts to create a simple app. All you need is to spawn three prompts (each prompt is an agent) in each of the new conversations in Agent Manager:

1. Researcher Agent:

Begin researching the Good Agent SDK and how to apply it to a Market Intelligence agent. The agent should help users monitor industry trends, competitor movements and emerging opportunities within a chosen market. We need to understand the proper usage of agent hierarchy and coordination, including how a Researcher Agent gathers data, how insights are stored in memory and how the system remains responsive to ongoing user queries.

2. Frontend Agent: 

Build a frontend UI for a market intelligence chat agent with mocked data. It should support document uploads (PDF reports, CSV datasets), basic chart visualizations and a clean UI/UX for reviewing insights, comparing competitors, tracking trends over time and managing saved research snapshots.

3. Backend Agent:

Begin a basic configuration of a Python FastAPI backend using Google’s Agent Kit (adk-python). Set up a minimal backend architecture that supports agent management and future expansion, starting with a simple health-check endpoint and placeholder routes for research queries and data ingestion.

All three agents worked in parallel. While the Researcher Agent was browsing Google's SDK documentation, the Frontend Agent was building React components and the Backend Agent was writing API routes. You don't have to wait for one to finish before starting the next.

Maybe you will feel like me. This was the first time I felt like I was actually managing a team, not babysitting an AI. With this AI coding tool, you're not a coder anymore; you're the boss of a team of AI engineers.

III. Feature #2: Asynchronous Feedback - The "Vibe Flow" Secret

This feature exposes an uncomfortable truth: most AI coding tools still waste your time. One small mistake and you’re told to “just start over.” This feature #2 breaks that pattern completely and lets you interrupt, correct or even redirect the AI without killing your flow.

1. The Frustration You've Definitely Experienced

You give an AI a detailed prompt. It gets 75% of the work right but then it adds three features you didn't ask for. In traditional AI coding tools, you either accept the boring output or restart the entire prompt from scratch. Both options kill your flow state.

2. The Antigravity Solution: Inline Editing During Execution

Antigravity's Asynchronous Feedback System lets you add corrections while the agent is planning or executing without stopping the process or starting over.

3. How It Works in Practice

Let’s say the Frontend Agent generates a Task List for building the UI that includes “Charts/Graphs” and “User Profile Section,” which you don’t need for the MVP.

Traditional workflow: Restart the prompt. "Actually, forget the charts…"

Antigravity workflow:

1. Click on the "Charts/Graphs" and “User Profile Section” checkbox in the Task List.

2. Leave an inline comment: "Remove these features from the MVP entirely".

3. Click “Add Comment”.

4. Finally, click "Submit".

The agent immediately updates its plan without failing the build. It adapts its scope dynamically: "Updating scope based on user feedback... Removing charts module…"

This is what “vibe coding” actually means. You’re not typing code. You’re directing work in real time.

IV. Feature #3: Artifacts - The "Plan, Refine, Manage" Framework

Have you ever let AI decide your output and turn out that the output is horrible? If your answer is yes, dang it, we’re in the same boat. I found the solution; it’s on this feature. This feature #3 adds a pause button between ideas and execution, so you guide the plan before the code gets written.

1. The Human-in-the-Loop Problem

When AI builds things on its own, the results are usually just okay. Without human guidance, it often makes design choices that don’t really make sense.

“Vibe coding” doesn’t mean letting AI run free. It means using AI for speed while humans handle taste, judgment and final decisions.

2. The Solution: Artifacts as "Proof of Work"

Artifacts are organized documents generated by agents that serve as checkpoints before code is finalized.

• Task Lists: High-level to-do lists.

• Implementation Plans: Detailed building steps.

• Walkthroughs: Step-by-step changelogs explaining what was changed and why.

3. Real-World Example: UI Revamp

I asked the Frontend Agent to revamp the UI (make sure you switch to the Claude Sonnet 4.5 model and Planning Mode).

Instead of immediately writing code, the agent generated an Implementation Plan Artifact that outlined the color palette and typography.

I opened the Artifact and left inline comments: "Make the primary color lighter" and "Use Poppins for headings". The agent updated the plan before writing a single line of code.

This is the "Plan → Refine → Manage" framework.

Result: Professional-grade code without the back-and-forth rework cycle.

V. Feature #4: Browser Automation - Self-Healing UI Testing

Building is easy. Testing is the part everyone hates. What if I tell you that I found a way to do that easier? Sounds cool, right? This feature #4 removes you from the loop entirely by letting AI open the browser, audit its own UI and fix anything that doesn’t pass the grade.

1. The Manual Testing Nightmare

You've built a UI. Now you need to verify it works. Traditionally, this means manually clicking through every feature and taking screenshots. This workflow is boring and easy to mess up. It feels like a hell loop that no one on this Earth loves.

2. The Solution: Integrated Chrome Browser Automation

Antigravity includes a headless (or visible) Chrome browser that agents can control to verify their own work.

3. How It Works

After the Frontend Agent finished building the chat interface, I gave it one command: "Launch the browser, audit the UI and grade it on a scale of 1-10. If it's below 8, recommend specific updates".

Here is what happened automatically:

1. Chrome opened and navigated to localhost:3000.

2. The agent clicked through the app (opened chat, sent a message, toggled dark mode).

3. An Audit Recording was generated.

4. The agent analyzed its own work: "Current UI Grade: 7/10. Issue identified: the Charts are still using the old, darker colors and don't match the new lighter UI variables".

5. I clicked "Proceed". The agent fixed the issue, re-audited and confirmed: "Updated Grade: 9.5/10".

This is a self-healing UI. The AI coding tool doesn't just build; it tests, critiques and fixes its own work.

VI. Feature #5: Custom Workflows - Your AI Standard Operating Procedures

If an AI coding tool makes you repeat yourself all day, it’s not smart. It’s just a chat box. This feature #5 turns your best prompts into reusable SOPs, so you run the same high-quality process every time.

1. The Repetition Problem

Re-typing detailed instructions like “Debug this issue systematically” or “Refactor this code following the Airbnb style guide” every time is exhausting.

2. The Solution: Custom Workflows (Slash Commands)

Custom Workflows let you store repeatable, structured processes that can be triggered via a command, like Notion slash commands but for AI management.

3. Real-World Example: Systematic Debugging Workflow

I imported a "Systematic Debugging" skill (from Github, shout out to Obra) that forces the agent to follow a 4-phase debugging process:

1. Root Cause Investigation: Analyze error logs.

2. Pattern Analysis: Check if similar issues exist elsewhere.

3. Hypothesis Testing: Formulate and test potential causes.

4. Implementation: Implement the fix and write a regression test.

Bonus: This file contains my content for the Debugging Workflow. Copy and paste it into your own workflow if you want.

Okay, let’s go back to my situation. My uploaded file is not working. Instead of typing a 200-word prompt, I just type: @debugging-workflow The file upload feature in our chat is not functional.

The agent ignores "quick fix" shortcuts and methodically works through all four phases. It prevents "whack-a-mole" debugging, where fixing one issue creates three more.

VII. Feature #6: Review Policies - The Setting 90% of Users Get Wrong

Giving any AI coding tool full freedom sounds smart until it deletes your entire project or even breaks your computer. That is a real use case that happened before on Reddit.

This feature #6 exists to get you away from that terrible way. Because agents are way more confident than they should be and almost never ask permission.

1. The Autonomy Paradox

AI autonomy is a double-edged sword. Too much and the agent makes bad changes. Too little and you're micromanaging.

2. The Critical Insight

The "Agent Decides" setting is misleading. Agents are over-optimistic about their own skills and almost never ask for review, even when making bad changes or major design changes.

That’s why, in the latest patch, Google removed this feature from the settings. Right now, we only see two options in the Review Policy:

• Always Proceed: The agent never asks for a review.

• Request Review: The agent always asks for a review.

3. The Actionable Fix

When starting a project or working on critical features, toggle on "Request Review". This forces the "Plan → Execute" strategy and allows you to use the Artifact feedback system effectively.

Ignore autonomy theory. Just remember this rule:

• Early stages: Request Review = ON.

• Mid-project: Request Review = OFF (use Asynchronous Feedback).

• Refactors: Request Review = ON.

This balance gives you speed and control.

VIII. Feature #7: Model Selection - Stop Wasting Credits

Running everything on the “best” model feels smart… until you check your usage bill (it's now empty like your future, joking). This feature #7 exists because most users (even for me) confuse power with efficiency and waste credits where they don’t matter.

1. The Default Mistake

Most users default to "newest = best" and run everything through Gemini 3.0 Pro, burning through credits unnecessarily.

2. The Three-Model Strategy

You need to understand that different models excel at different tasks.

• Gemini 3.0 Pro (The Orchestrator): Best for multi-agent management, Artifacts generation and Browser Automation. It is optimized for Antigravity's architecture.

• Claude Sonnet 4.5 (The Deep Thinker): Best for complex debugging, algorithmic problems and fixing old code. It generally has stronger logical reasoning for pure code.

• GPT-OSS (The Janitor): Best for simple tasks like documentation, formatting and generating standard code. It is cheaper and "good enough".

So, before you get started on any tasks, you need to review which models you should use and where to use them to get the best result, saving your money and time.

If you’re interested in other models, make sure you’ve checked the Antigravity documentation before using them.

3. My Workflow

Before giving you my Cost-Saving Rule of Thumb, you need to know the price for each model.

That’s why my strategy looks like this:

• Agent Manager & UI builds: Gemini 3.0 Pro.

• Complex debugging: Switch to Claude Sonnet 4.5.

• Documentation: Switch to GPT-OSS.

• Result: Faster builds, better code, lower costs.

If you run everything on the “best” model, you burn credits fast. If you match the model to the task, you ship faster and cheaper. This is one of the easiest wins most Antigravity users miss.

So yeah, that’s it. Here is the systematic workflow I used to build production apps at breakneck speed with this AI coding tool:

1. Plan: Spawn agents via Agent Manager. Set Review Policy to "Request Review".

2. Refine: Review Artifacts (Task Lists, Implementation Plans). Leave inline comments.

3. Manage: Agents execute refined plans in parallel. Monitor via Agent Manager.

4. Verify: Agent launches Chrome, audits the build and self-grades.

5. Systematize: Use Custom Workflows (@command-name) for repetitive tasks.

6. Optimize: Switch models strategically based on the task difficulty.

IX. Conclusion: The Unfair Advantage

The people who win with AI coding tools won’t be the ones writing the most code. They’ll be the ones who know how to manage work better.

That’s why I don’t see Antigravity as “better than Claude” or “faster than ChatGPT.” It changes your role completely; you stop acting like a coder and start acting like a manager (like a boss).

While most people are still stuck in linear chats, waiting for one answer at a time, you can spin up multiple agents, run tasks in parallel and ship real apps at a pace that looks unreal from the outside.

This isn’t a future idea. It’s already happening if you use Antigravity the right way.

So my question to you is simple: Are you going to learn how to manage AI or keep writing code line by line like it’s still 2020?

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