Stop Coding, Start Managing: A First Look at Google's AntiGravity IDE 🚀

TL;DR: I took Google's new agentic IDE, AntiGravity, for a spin. It shifts the workflow from writing code to managing asynchronous agents. In this post, I break down how I built a Stock Dividend Tracker using Google Antigravity and Gemini 3, parallel task execution, and the tool's really cool feature: verifiable screen recordings.

The biggest difference in AntiGravity is the interface. While it has a traditional code editor (VS Code style) on the left, the right side is dominated by the Agent Manager.

Think of the Agent Manager as an autonomous coding engine. Instead of pair programming, you are assigning tasks.

• The Inbox: This is your command center. You spin up tasks and check your inbox for status updates.
• Modes: You can toggle between Planning Mode (for complex architecture) and Fast Mode (for quick fixes).
• Models: You can swap models per task (e.g., using Gemini 3 for logic and a faster model for boilerplate).

The Killer Feature: Verifiable Artifacts 📹

We’ve all had an LLM tell us "Task Complete," only to run the code and watch it crash.

AntiGravity solves this with browser tool. When the agent claims it built a feature, it doesn't just show you the code—it proves it works.

1. Browser Control: The agent spins up a browser instance (you can see the "blue halo" indicating agent control).
2. DOM Interaction: It clicks buttons, enters data, and navigates the UI.
3. Screen Recording: It records a video of the test and analyzes that video to verify success.

In my demo, I asked it to build a Dividend Tracker. It didn't just write the API fetch; it opened the browser, added Apple and Microsoft stocks, verified the yield calculation, and sent me a video recording of the successful test.

The Workflow: Asynchronous & Parallel

Because the Agent Manager acts like an employee, you can multitask.

In the demo, I reached a point where the core app was working. I then issued two parallel tasks:

1. Thread A: Update logic to calculate total dividends based on share count.
2. Thread B: Create a visualization (Pie Chart) of the portfolio.

I sent both requests, stepped back, and monitored the Inbox. Both tasks ran simultaneously. The agent updated the implementation plan, modified the code, verified the output, and notified me when done.

Human-in-the-Loop Configuration

For those worried about an AI running rm -rf / on your machine, AntiGravity allows for granular permissions. You can set the Review Policy:

• Always Approve: For low-stakes environments.
• Ask for Approval: The agent pauses and asks permission before running CLI commands or critical file changes.
• Auto-detect: The agent decides based on task complexity.

The experience felt less like "coding" and more like "code reviewing." I spent my time editing the Task List and Implementation Plan documents rather than wrestling with syntax.

Final Thoughts

AntiGravity represents a shift from writing software to architecting software. The ability to trust the output because of the verifiable artifacts (screen recordings) is a massive step forward in reducing the friction of AI development.

Check out the full video below to see the agent driving the browser in real-time!

Have you tried Agentic workflows yet? Let me know in the comments!