Antigravity is Google's attempt to answer a question most AI coding tools dodge, which is what an editor should look like once the model writes most of the code. The answer here is a surface where you supervise several agents rather than sit inside a single file, with an editor view for when you want to work directly and a manager view for when you want to dispatch work and check on it. The getting started material walks you through both cleanly and it takes about an hour to feel oriented. The manager view is the part worth paying attention to.
Most tools give you a chat panel next to your code and call it agentic, which keeps you in the loop on every step and caps how much the agent can usefully do. Running several tasks in parallel and reviewing them as they land is a different working model, and the documentation does a reasonable job of showing the mechanics of it. Whether that model suits you depends heavily on the kind of work you do, and the material has no opinion on that at all. The artifact concept is the strongest idea in the product.
Instead of a wall of tool calls, agents produce plans, task lists, screenshots and recordings of what they did, which means reviewing the work is closer to reading a pull request than to archaeology. Anyone who has scrolled back through a thousand lines of agent output trying to work out where it went wrong will appreciate why this matters. The documentation covers it well and it is the feature I would point at if someone asked what is actually different here. Browser control deserves credit too.
An agent that can open the app it just changed, click through it and take a screenshot is verifying its own work in a way that a code diff never does, and the material explains the setup properly. It is not foolproof, since an agent can look at a broken page and report success, but it catches a category of mistake that used to reach you. Where the documentation falls short is judgement. It teaches you how to run five agents and never addresses whether you should, what kinds of task split well, or how to decompose work so parallel agents do not collide.
Those are the decisions that determine whether this is faster than doing it yourself, and they are left entirely to you. Cost and quota guidance is similarly thin. Parallel agents burn through model capacity quickly, the free tier has limits that arrive sooner than the marketing implies, and the material does not prepare you for the moment your workflow stalls because you have run out. Anyone planning to use this seriously should find out early what their actual ceiling is.
The review problem is the one I keep coming back to. An agent that produces more output than you can read carefully has moved the bottleneck rather than removed it. Artifacts help. They do not solve it.
The honest position is that you still have to read the code, and running more agents means reading more code, and nothing in the documentation acknowledges that trade. Three point six. Real architectural thinking about a genuinely unsolved problem, with documentation that teaches the tool competently and stays quiet on every question about when and whether to use it this way.