Google Antigravity Review: DeepMind’s Agent-First Bet on Faster, Safer Software Development
Antigravity is Google DeepMind’s push toward an agent-first way of building software. Instead of treating AI like a smarter autocomplete, it treats AI like a capable collaborator that can take on real tasks, run with them, and come back with something you can review.
What sets Antigravity apart is how it brings three surfaces into one workflow: a familiar editor for hands-on control, an Agent Manager that coordinates multiple autonomous tasks (so work can happen in parallel), and browser integration so agents can pull in web context and validate what they just built.
I tested Antigravity myself in a small project, and it genuinely surprised me: in a short session, I went from “idea” to a working web app with far less manual glue work than I expected. That hands-on experience is what made me want to break the workflow down, because it feels less like a novelty and more like a shift in how you run development.
Table Of Contents
Understanding the tool: One Workflow Across Three Surfaces
Antigravity is easiest to understand if you stop thinking “IDE with an assistant” and start thinking “one workflow split across three surfaces.” Each surface is optimized for a different part of modern dev work: delegating tasks, doing hands-on edits, and validating behavior in a real browser, without constantly bouncing between apps.
Agent Manager
The Agent Manager is the coordination layer. It is where you create, run, and monitor agents across multiple workspaces, especially when you want work happening in parallel. Think of this as mission control for agent workstreams.
If you’re wearing a CTO/Leader hat, this is the part that changes the rhythm, less “babysit one task” and more “dispatch, review, iterate.” That’s exactly how it felt in my own use: I’d kick off a task, let the agent run, and then come back to something reviewable instead of staying in a single chat thread.
That shift can be valuable when you are juggling multiple initiatives and need predictable checkpoints.