My Experience with Google Antigravity: How I Refactored Easy Kit Utils with AI Agents 🚀

Source: Dev.to

Overview

Hey everyone! 👋

Google just dropped something pretty wild into the dev‑tools arena: Google Antigravity, their new AI‑native IDE powered by Gemini 3. It’s been a game‑changer for how I approach coding. I recently used it to completely refactor Easy Kit Utils, and I wanted to share my thoughts on this new tool that’s got everyone talking.

If you’ve been following the AI‑coding‑assistant space, you’ve probably heard the buzz around Cursor, Windsurf, and other tools. Google decided to enter the chat with a different approach: instead of just assisting you while you code, Antigravity lets autonomous agents do the heavy lifting while you act more like an architect than a bricklayer.

Let’s start! 🤙

What Actually Is Google Antigravity? 🤔

First things first, let’s clear up what we’re dealing with. Antigravity isn’t just another VS Code fork with AI bolted on. It’s built from the ground up as an “agent‑first” development platform. What does that mean in practice?

• Instead of an AI chatbot sitting in a sidebar suggesting completions, Antigravity gives you autonomous agents that can work across three main surfaces: the code editor, the terminal, and an integrated browser.
• These agents don’t just write snippets—they plan entire workflows, execute complex tasks, and even validate their own work by testing in the browser.

The core concept Google is pushing is what they call “vibe coding.” The idea is simple but powerful: describe what you want in natural language, and the agent figures out the implementation details. No more wrestling with syntax or hunting through documentation for that one specific method you need. Just tell it what you’re trying to achieve, and it handles the rest.

When you launch Antigravity, you’re not greeted with a file tree like traditional IDEs. Instead, you see the Agent Manager, a mission‑control dashboard where you can spawn multiple agents working on different tasks simultaneously. It’s like having a team of developers working in parallel—except they’re all AI.

The Strengths: Where Antigravity Really Shines ✨

After spending significant time with Antigravity while refactoring Easy Kit Utils, here are the areas where it genuinely impressed me:

Autonomous Task Execution

This is the big one. When I started the refactor, I could give Antigravity high‑level objectives like:

“Refactor the string utilities to use modern ES6+ syntax and add comprehensive type definitions.”

The agent broke this down into subtasks, created an implementation plan, and executed it. I wasn’t writing every line; I was reviewing and approving the agent’s work. It read through existing code, understood patterns, made changes across multiple files, and even updated related tests. It felt less like coding and more like managing a very capable junior developer.

Multimodal Understanding

One thing that caught me off guard was how well it handles visual inputs. I could literally show it screenshots of design mockups or UI components and say, “Make it look like this.” The agent understood the aesthetic and implemented it. Gemini 3’s multimodal capabilities are strong, but having them integrated directly into the IDE workflow is surprisingly useful.

Browser Integration

The built‑in browser is clutch. The agent can test web applications it builds, interact with DOM elements, and verify that everything works as expected. While updating documentation examples, the agent spun up a test page, verified the utilities, and even caught edge cases I hadn’t thought about.

Planning and Reasoning

Gemini 3’s planning capabilities are legitimately good. For complex refactoring tasks, the agent created a step‑by‑step plan before touching any code. I could review the approach first and make adjustments, which feels like pair programming with a partner who thinks through the problem before diving in.

Multi‑Agent Workflows

The inbox system for managing multiple agents is elegant. While one agent refactored utility functions, I spawned another to update documentation and a third to improve test coverage. They all worked independently and pinged me when they needed input or approval.

The Weaknesses: Where It Falls Short 🤷

Nothing’s perfect, and Antigravity definitely has its rough edges. Here’s what frustrated me:

The Learning Curve Is Real

Despite Google’s marketing about “natural language is the only syntax you need,” there’s still a learning curve. You need to:

• Structure prompts for optimal results.
• Know when to use agent‑driven vs. review‑driven modes.
• Manage the Agent Manager effectively.

The interface is overwhelming at first. There’s the Agent Manager, the editor, the browser panel, and various settings for controlling agent autonomy—all of which take time to understand. My first hour was spent just figuring out the basic workflows.

Performance Can Be Inconsistent

Gemini 3 Pro is subject to rate limits and quotas, especially in the free preview period. I hit multiple “model provider overload” errors during peak usage times. When you’re in a flow state and the agent suddenly stops because of API limits, it kills momentum.

There’s also latency. Waiting for an agent to plan, execute, and validate can sometimes feel slower than just writing the code yourself, especially for simple tasks. The trade‑off is only worth it for more complex operations.

Over‑Automation Can Be Dangerous

The agent‑driven mode is powerful but risky. If you’re not careful, the agent can:

• Make sweeping changes across the codebase without your full awareness.
• Introduce subtle bugs that are hard to trace because the reasoning steps are hidden inside the model.

It’s essential to keep a tight review loop and treat the agent as an assistant, not a replacement for human oversight.

Bottom Line

Google Antigravity is a bold step toward agent‑first development. When used wisely, it can dramatically accelerate complex refactors, UI work, and multi‑task projects. The current pain points—learning curve, performance limits, and the need for vigilant review—mean it’s not yet a plug‑and‑play solution for every developer. But for those willing to invest the time to master its workflow, Antigravity feels like a glimpse of the future of coding.

Still Makes Mistakes

Let’s be honest: the agent isn’t infallible. It still generates code with bugs, makes questionable architectural decisions, and sometimes misunderstands what you’re asking for. You absolutely need to review everything it produces. It’s like working with a capable junior dev who needs oversight, not a senior engineer you can fully trust.

I found subtle issues in type definitions, incorrect error handling in edge cases, and occasionally methods that didn’t follow the existing conventions of the codebase. These are all fixable, but it means you can’t just set it and forget it.

Limited Third‑Party Integration

Unlike Cursor, which builds on VS Code’s massive extension ecosystem, Antigravity is more of a walled garden. You can’t just install your favorite VS Code extensions. The tooling is what Google provides, and while it’s good, you might miss your preferred linters, formatters, or other productivity tools.

My Real‑World Experience: Refactoring Easy Kit Utils 💪

Now for the practical stuff. I used Antigravity to refactor Easy Kit Utils, a JavaScript utility library I maintain under Domenico Tenace Open Labs. The library needed modernization, updating to newer JS syntax, adding TypeScript definitions, improving test coverage, and generally cleaning up accumulated technical debt.

How It Went Down

1. Initial setup – I gave the agent access to the repository and described the overall goals.
2. Planning – The agent read through all the files, understood the structure, and proposed a refactoring plan broken into logical phases. This alone was impressive: an AI that could comprehend a full codebase and suggest a structured approach.
3. Utility‑by‑utility refactor – For each utility function, I asked the agent to modernize it while maintaining backward compatibility. It refactored the code, updated the associated tests, regenerated documentation examples, and verified everything worked.
4. Iterative feedback – I reviewed the changes, provided feedback, and iterated until the output matched what I wanted.

The biggest win was the agent’s ability to maintain consistency. When refactoring string utilities, it applied the same patterns to array utilities without me having to repeat instructions. It learned the style and conventions I preferred.

The multimodal aspect helped with documentation, too. I could show it how I wanted code examples formatted, and it applied that formatting across all the documentation files.

What Fell Short

• I had to roll back some changes that broke edge cases.
• Type definitions needed manual tweaking.
• Certain implementations were technically correct but stylistically different from the rest of the codebase.

Overall, what would have taken me a week of tedious refactoring was done in a couple of days, with most of the time spent on review rather than actual coding.

The Verdict: Should You Try It? 🎯

Look, Google Antigravity is fascinating tech, and it represents a genuine shift in how we might code in the future. But is it ready to replace your current workflow? That depends.

You Should Definitely Try Antigravity If

• You’re working on projects that need significant refactoring or modernization.
• You want to experiment with agent‑first development workflows.
• You’re comfortable reviewing AI‑generated code thoroughly.
• You have the patience to learn a new development paradigm.
• You’re building web applications that benefit from browser‑based testing.

Maybe Stick With Your Current Tools If

• You need rock‑solid reliability without API rate limits.
• You’re deeply invested in a specific IDE’s extension ecosystem.
• Every line of code must be hand‑crafted.
• You prefer traditional development workflows.
• You need consistent performance without latency issues.

For me, Antigravity has earned a place in my toolkit, but it hasn’t replaced everything else. It’s incredible for large refactoring tasks, prototyping new features, and handling repetitive coding work. For precision work, debugging complex issues, or when I need complete control, I still reach for my traditional IDE.

Some Positive Thoughts to Wrap Up 🌟

Despite the criticisms, I genuinely think Google is onto something here. The agent‑first paradigm feels like a natural evolution of AI coding assistants. Instead of treating AI as a fancy autocomplete, we’re moving toward AI as collaborative team members that can handle entire workflows.

The fact that I could refactor an entire library with significantly less manual effort is remarkable. Even with the rough edges, the productivity gains are real. As someone who maintains multiple open‑source projects, anything that helps me tackle technical debt faster is valuable.

The multimodal capabilities open up interesting possibilities. Being able to communicate through code, text, and visuals makes the interaction feel more natural and expressive than pure text‑based chatbots.

And here’s something often overlooked: the learning experience. Using Antigravity forced me to think more architecturally about my code. When you describe goals to an AI agent rather than implementing them yourself, you naturally focus on the “what” and “why” rather than the “how.” This higher‑level thinking has actually improved how I approach problems even when I’m not using the tool.

Google’s also iterating fast. The issues I mentioned—rate limits, performance, rough edges—are all things that can improve over time. We’re in the early preview phase, and if Google continues investing in this platform, it could become really powerful.

What’s Next for Me? 💫

I’m planning to continue using Antigravity for appropriate tasks while keeping my traditional IDE for others. The key is understanding which tool fits which job.

For Easy Kit Utils, the refactoring is now complete thanks to Antigravity’s help, and the library is in much better shape. TypeScript definitions are solid, the code is modernized, and test coverage is improved. Now I can focus on adding new features rather than dealing with technical debt.

I’m also curious to experiment more with the multi‑agent workflows. The idea of having multiple AI agents working on different aspects of a project simultaneously is compelling, even if the execution is still rough around the edges.

And honestly? I’m just excited to see where this technology goes. Whether Antigravity specifically becomes the dominant tool or not, the concept of agent‑driven development is here to stay. We’re watching the future of coding take shape in real‑time.

Conclusion

Google Antigravity is bold, ambitious, and imperfect. It’s not a “Cursor killer” or a revolutionary replacement for all your dev tools, but it is a genuinely interesting experiment in how AI can be integrated into the development workflow at a fundamental level.

If you’re a developer who likes exploring new tools and isn’t afraid of some rough edges, absolutely give it a try. The experience of working with autonomous coding agents is both enlightening and occasionally frustrating, but definitely worth experiencing.

For open‑source maintainers like me who need to juggle multiple projects, anything that helps tackle maintenance tasks more efficiently is worth exploring. Antigravity helped me ship a better version of Easy Kit Utils faster than I could have done manually.

Is this the future of coding? Maybe—parts of it, at least. We’re still figuring out what works and what doesn’t. But that’s what makes it exciting.

So yeah, go download it, try building something, break it, learn from it, and let me know what you think!

Happy coding! ✨

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My name is Domenico, a software developer passionate about Open Source. I write articles to share my knowledge and experience.

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