Google Antigravity Explained: From Beginner to Expert Guide

AI tools used to live in the margins: autocomplete, a chat sidebar, a “helpful” assistant that waited for you to ask nicely.

Google Antigravity signals a more important shift. It’s designed around the idea that the AI shouldn’t just suggest code. It should be able to plan, execute, and verify multi-step work across your editor, terminal, and browser, while you supervise at the right moments.

The future of AI agents isn’t the model. It’s the workflow. Permissions. Review. Proof. Guardrails. Feedback loops. Observability.

This guide breaks Antigravity down from beginner to expert, then ends with the question that matters for operators:

what should we copy when we deploy agents into real customer-facing systems?

Beginner: What is Google Antigravity?

Google Antigravity is an agentic development platform built for the “agent-first” era. The emphasis is on outcomes, not snippets.

Instead of “help me write this function,” the mental model becomes “complete this task end-to-end.”

Antigravity combines:

a familiar Editor experience for hands-on work, and
an Agent Manager (often described as “mission control”) for managing agents that can work more autonomously.

If you’re coming from a “chat in the sidebar” tool, the difference is the product thesis:

Agents shouldn’t be a sidebar feature. They should have a workspace.

Beginner: The core loop (Plan → Execute → Verify)

Most AI coding tools optimize for speed: “write faster.”

Antigravity optimizes for outcomes: “finish the task.”

At a high level, the agent workflow looks like this:

Plan the work (what it will do, and why)
Execute the steps (edit files, run tools)
Verify results (tests pass, UI behaves, behavior is validated)

That “verify” step is where agentic systems either earn trust or lose it. If you’ve ever merged a “looks right” fix that didn’t actually work, you already understand why verification is not optional.

Intermediate: Two views that match how humans actually work

Antigravity is organized around two primary surfaces, and that’s a subtle but important design choice.

1) Agent Manager (mission-control mode)

When you open Antigravity, you’re often greeted by the Agent Manager first. This is the “manager mindset” view: assign work, monitor progress, and review outputs.

It’s built for asynchronous operation, where multiple tasks can be running without you babysitting each one.

2) Editor (hands-on mode)

When you want to drive, you switch to the Editor. The point isn’t that you stop coding. The point is that you can move fluidly between:

hands-on building, and
high-level orchestration.

That’s the first agentic UX lesson worth stealing: give humans both control modes, and let them switch instantly.

Intermediate: “Artifacts” are the trust layer most agent products are missing

If you only take one idea from Antigravity, take this:

Don’t ask humans to trust agent output blindly. Give them proof they can review quickly.

Antigravity calls that proof Artifacts: tangible deliverables produced during planning that allow the agent to communicate progress asynchronously.

Instead of forcing you to read raw logs, you review the agent’s work through structured outputs.

Examples of Artifacts include:

implementation plans
task lists
walkthroughs for verification
screenshots
browser recordings

This matters because raw agent logs don’t scale. They’re too detailed, too time-consuming to audit, and too easy to misread when you’re moving fast.

Artifacts are the alternative: reviewable receipts.

Even better, the workflow supports feedback directly on those artifacts (think: comment-on-the-plan), so you can correct direction without rewriting the whole request or breaking the agent’s flow.

Intermediate: Autonomy isn’t a switch. It’s a dial (Off / Auto / Turbo)

Agents become risky the moment they can take actions. Antigravity treats autonomy controls as a core product surface, not an afterthought.

During setup, Antigravity defines a Terminal Execution Policy with three modes:

Off: never auto-execute terminal commands (except allowlisted commands)
Auto: the agent decides; prompts you when permission is needed
Turbo: always auto-executes (except denylisted commands)

It also includes a Review policy for artifacts:

Always Proceed: agent never asks for review
Agent Decides: agent chooses when to ask
Request Review: agent always asks for review

This is a mature view of autonomy. You don’t have to choose between “manual” and “wild west.” You choose your risk posture, and you can change it as your confidence increases.

Advanced: Browser-in-the-loop agents (and why verification changes)

Antigravity can interact with Chrome as part of its workflow. In practice, that means it can use a browser subagent to:

open and control pages,
extract information from the web,
automate browser tasks,
and capture screenshots or videos as part of verification.

A key implementation detail: it runs in a separate browser profile so agent activity is isolated from normal browsing. That’s a thoughtful safety decision, and it makes verification more concrete.

This is where “verify” becomes real. You’re not just told “it works.” You can review what happened through an artifact trail.

Advanced: Safe-by-default browsing (allowlists and denylists)

The moment an agent can browse, it can also encounter hostile content: prompt-injection attempts, malicious scripts, or poisoned documentation pages.

Antigravity’s browser security approach includes allowlist/denylist controls. One practical detail: the allowlist starts with localhost, and navigating to a non-allowlisted URL can trigger a prompt with an option to “always allow,” which then adds it to the allowlist.

This is the second agentic UX lesson worth stealing:

Make the safe behavior the default behavior. Make risky expansion explicit.

Expert: Scaling is orchestration

Once agents can do real work, your bottleneck shifts. The question stops being “can the AI do this task?” and becomes:

Can you monitor work without babysitting?
Can you review outcomes quickly?
Can you route tasks and feedback efficiently?
Can you enforce policy and audit behavior?

That’s orchestration. That’s operations.

Antigravity’s Agent Manager approach makes this explicit: agentic systems need “mission control,” not just a chat box.

Expert: Learning is powerful, but only if it’s governed

Antigravity’s design direction treats learning as a core primitive, including the ability to save useful context and snippets so agents improve over time.

That’s powerful, and it raises expert-level questions that every agent team eventually faces:

What gets saved?
Who approves it?
How do you prevent bad “learnings” from becoming policy?
How do you avoid sensitive data leaking into reusable memory?

The best systems separate:

Knowledge (approved sources),
Experience (suggestions the agent proposes),
Execution (actions the agent is allowed to take).

In other words: learning is valuable only when it’s governed.

Expert: Practical constraints (rate limits are part of the product)

Agents consume capacity because they do “work,” not just text generation. Antigravity’s plan-based limits reflect that reality, with quotas that refresh on different cadences depending on plan and usage.

Operationally, this matters. Mature agent deployments need to think about:

throttling,
graceful degradation,
fallbacks,
and what happens when you hit limits mid-workflow.

If you’re deploying agents in production, reliability isn’t just model quality. It’s systems design.

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