There are two ways to use a powerful AI coding agent like Google Antigravity.
The first is to walk in with a thoroughly defined plan, tightly scoped instructions, properly framed rules, and realistic expectations.
The second is to do what I did.
I recently used Google Antigravity for updating my portfolio website, and the experience can be summarized like this:
Seamless. Insanely fast. Mind-boggling. I could never code at this speed !!!
Followed immediately by:
Wait. Why does this look like that?
It was THE MOST IMPRESSIVE yet EYE OPENING coding experiences I’ve had.
The Portfolio project
The goal was to build a good looking portfolio website to record my progress so far, while familiarizing myself with Google Antigravity.
This is the stack I decided to use, after consulting AI on various options:
- Next.js 16 for fast rendering and smooth transitions
- TypeScript 5.9 so the code doesn’t collapse from bad assumptions
- pnpm because disk space deserves rights too
- Tailwind CSS for quick styling
- 8px grid system because professionalism is apparently measured in multiples of eight
- Glassmorphism and dark mode because if it doesn’t look futuristic, did you even build a portfolio?
- A custom AnimatedSection component using the Intersection Observer API
- Markdown-based content with gray-matter ( I wanted to manually edit the content through placeholders in markdown files before deploying it )
- Deployment planned for Vercel, the natural habitat of portfolio websites
This was my first time working on such a multi-layered project, but thankfully I had Gemini at my side.
On paper, this looked elegant.
In practice, it became a fascinating tug-of-war between me and an extremely fast machine.
The first 10 minutes: pure automatic magic
I have to give Antigravity credit where it’s due.
It produced the initial website in around 10 minutes after I did some basic environment setup, which is satisfying, mind boggling and absurd in the best way possible. Watching the agent generate the whole website genuinely felt like the future had arrived early and brought Tailwind with it. I wouldn’t have believed it if I hadn’t seen it with my own eyes.
For a brief shining moment, I believed I had just witnessed the holy grail of software development
Then I actually opened the website and looked at it.
The next hour: me vs. my inexperience and lack of planning
That’s when the real lesson hit me:
I had excitedly jumped into AntiGravity without a properly detailed design plan for each page.
And Antigravity, being an obedient and highly capable agent, did exactly what a powerful system does when the human is vague: it filled in the blanks.
Confidently.
Creatively.
Incorrectly, according to my personal taste.
So while it gave me a fully usable first version very quickly, I then had to spend about an hour morphing it into something that actually showed who I am, and my cough cough highly refined taste. Not because the agent was bad, but because my prompt had left too many blanks for it to fill in. And flexibility without specificity is how you end up saying things like:
“Technically this is good, but I guess it could be better.”
Finally after a few hours of changing the AI’s initial creation this is what I got:
I have to make some changes in the projects section, after which I will host it on Vercel and put the link here.
My Mistake #1: Vague prompts produce beautifully efficient Chaos
My biggest takeaway is simple:
Do not start with vibes only. Give your ideas proper structure.
Before using Antigravity for something like web dev, define things like:
- what exactly each page should contain
- what sections go where and how do i make each one pop
- what colour schemes should be used
- what kind of spacing, typography, and visual hierarchy you want
- what should definitely NOT happen
Because if you don’t define it, the agent will assume. And once the AI has “helpfully” made 37 design decisions on your behalf, you’ll be the one undoing them one by one, wasting quota and breaking your head over it.
A good prompt is not a luxury here. It is a MUST HAVE.
Mistake #2: Not setting rules for the agent
This one is huge.
Don’t just tell the agent what to build. Tell it how to behave while building.
Go one step further and set rules. Actual rules.
Things like:
- keep components modular
- don’t over-engineer
- reuse existing styles
- avoid unnecessary dependencies
- maintain consistent spacing
- prioritize readability over cleverness
- write code that is easy to edit manually later
Without guardrails, agents sometimes code like they’re trying to win an award for Most Enthusiastic Interpretation of a Request.
With rules, they’re far more useful.
Antigravity lets you manually specify these UNBREAKABLE rules in agent customizations. Don’t just put them in the prompt like I did.
Here’s a screenshot of where I changed it later on:
Mistake #3: using the WRONG model and burning through tokens
I also ran out of tokens much faster than expected.
This was partly because I used the wrong models for my simple job. Not every task needs the smartest, most expensive, most computationally dramatic option available.
One practical rule I will now implement is:
Use the smallest model that can handle the task, until it breaks.
That’s a much better strategy than sending a premium model to do the equivalent of rearranging padding and renaming a button component.
Save the stronger models for actual hard problems. Don’t waste your quota and cry till it refreshes.
Fixing errors: stop doing one at a time
Another lesson: don’t micromanage every tiny issue in separate prompts if you can avoid it.
If there are multiple errors, weird styles, broken components, or consistency issues, bundle them together.
Instead of:
- fix this one error
- now this one
- now this one too
- actually also this thing
Try:
Here are 10 issues, fix them in one pass and preserve the current design direction
This can save time, reduce prompt overhead, and probably save quota too. It also helps the model see patterns across issues instead of treating every bug like a totally unrelated act of God.
Agent Manager vs. manual prompting
The best way to vibe code on Antigravity is using Agent Manager / Mission Control effectively. Not the small agent next to the editor.
The difference is basically this:
In the normal editor-style workflow, you keep doing everything step by step:
Outline, code, tweak, test, fix, document, refine.
Each step is a new prompt. Each prompt costs quota. And each shift in attention forces context switching for both you and the model wasting time.
With Mission Control, the idea is more like:
Define the requirement clearly, assign the overall objective, make several agents split the work, and only check major milestones and focus on decision making.
I haven’t personally used the Mission Control side deeply enough to make bold claims, but conceptually it makes a lot of sense. It gives each agent their own fixed context/space in the project and one agent doesn’t have to waste time going back on forth between different areas.
Turbo Mode: excellent, unless your agent is quietly losing its mind
A very important warning from personal experience:
Check on your agents once in a while if you’re using Turbo Mode.
At one point, I had completely forgotten to install Node.js and Git Bash, and my agent got stuck in a loop trying to run terminal commands that had absolutely no chance of succeeding.
It kept trying. Repeatedly. Heroically. Futilely. It Never Gave Up.
And my quota was the one paying for that optimism.
So yes, autonomy is great. But occasionally peeking in to verify that the agent is not trapped in a digital version of pushing a door marked “pull” is a very good idea.
What Antigravity is actually good for
Despite the chaos, the bigger picture painted by Antigravity is exciting.
The coding landscape is changing fast. The bottleneck is no longer purely “how many languages do you know” or “how quickly can you manually debug syntax errors at 2 AM.”
What matters more now is:
- understanding what tools work well together
- knowing how to structure projects
- being able to evaluate outputs critically
- orchestrating workflows
- and above all, prompting REALLY REALLY WELL
That last one is not hype. It’s increasingly a real skill.
A decade ago, one person might have spent an hour making a PowerPoint about a product idea.
Now, that same hour can be used to build an end-to-end prototype of the idea itself.
That is a massive shift.
But no, you still cannot completely outsource understanding
This part matters.
AI can write a lot of code. It can scaffold systems ridiculously fast. It can even create frameworks that look AMAZING.
But when things break — and on large projects, things will break at one point— the person who wins is still the one who understands what’s going on. If no one knows what the code was, who will fix it?
So while using agent-generated code, spend time manually inspecting it.
At least understand the framework structure.
Try to catch redundancies.
Notice risky patterns.
See where complexity is growing for no reason.
Because if your project becomes a giant mystery box held together by AI confidence and your own hope, debugging it when it fails will be impossible.
Key Points to use Google Antigravity effectively
Here’s the condensed version of the whole reflection:
- Go in with a concrete plan for every single thing.
- Write prompts with all kinds of specifics, not just broad intent.
- Set rules for code quality and agent behavior.
- Use smaller models first; escalate only when needed.
- Batch errors and fixes instead of handling everything one by one.
- Use Agent Manager for larger, parallel workloads when possible.
- Monitor autonomous agents occasionally, especially in Turbo Mode.
- Always understand the codebase enough to step in when things go wrong.
Or even more simply:
Antigravity works best when you think before the agent thinks for you.
Final reflection
Using Google Antigravity made one thing very clear to me:
The future of building is less about manually doing every step yourself and more about directing agents effectively.
The developers who thrive won’t be the ones who can code everything from scratch. They’ll be the ones who can design workflows, communicate intent clearly, spot bad outputs quickly, and guide intelligent tools toward useful results.
So yes, AI can build fast.
Sometimes terrifyingly fast.
But the human still matters — especially the human taste, judgment, and ability to say:
“No, that button absolutely should not be doing that.”
Some Other Useful Resources
Mastering the Antigravity Agent Manager by Kotcyn : https://youtu.be/zP_9mJ5vv_8
Google Antigravity Agent Manager Explained: Deep Dive : https://www.arjankc.com.np/blog/google-antigravity-agent-manager-explained/
Google Antigravity Model Quotas : https://antigravity.google/docs/plans
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