Google Antigravity: A New AI-powered Development Platform for Software Engineers

Nov. 21, 2025 /Mpelembe Media/ — Despite the name, Google Antigravity is not a physics experiment; it’s a revolutionary new AI-powered development platform for software engineers.

It’s an “agent-first” system that uses autonomous AI agents (primarily powered by Gemini 3 Pro) to plan, execute, and verify complex software tasks across the editor, terminal, and browser. It shifts the developer’s role from writing every line of code to acting as an architect or orchestrator.

Key Concepts of Google Antigravity

Concept | Explanation | Why it Matters
Agent-First Paradigm | The AI is an autonomous actor that can take a high-level task (e.g., “Add a user login page”) and break it down into steps, write code, run tests, and verify the outcome. | Moves beyond simple code suggestions (like an assistant) to full task delegation.
Dual Workspaces | It offers two main views: Editor View (a familiar IDE with an agent sidebar) and Manager View (a “mission control” to spawn, monitor, and orchestrate multiple agents simultaneously). | Enables developers to multitask and manage asynchronous, parallel workstreams.
Cross-Surface Autonomy | Agents have direct access and control over the code editor, the terminal (to run commands/install dependencies), and an integrated browser (to test and verify the UI). | Allows agents to complete end-to-end tasks without constant manual context-switching.
Artifacts for Trust | Instead of raw logs, the agents produce verifiable deliverables called Artifacts, such as implementation plans, task lists, screenshots, and browser recordings. | Builds trust and transparency by showing the user what the agent did and how it verified its work.
Feedback Loop | Developers can provide feedback directly on the Artifacts (like adding a comment on a Google Doc), and the agent will incorporate this feedback into its ongoing work without stopping the entire process. | Makes it easy to correct the agent and iterate on complex tasks.

Case Study Example

While specific, official enterprise case studies are still emerging as the platform is in public preview, one frequently cited example is the rapid development of a full-stack application.

Case: Developing a Simple Financial Tracker App

Step | Traditional Development | Antigravity Agent-First Development
Initial Prompt | A developer manually sets up the project structure, configures the database, and begins coding the core features. | The developer provides a single, high-level prompt: “Build a finance tracker application with income and expense management, database persistence, and user authentication.”
Execution | The developer spends days/weeks coding backend APIs, writing frontend components, integrating the database, and manually writing test suites. | The Gemini 3 Pro agent: 1. Plans: Generates an Artifact (a detailed task list/plan) for review. 2. Codes: Writes all necessary files (backend, frontend, database schema). 3. Tests: Uses the terminal to spin up the local server. 4. Verifies: Uses the integrated browser to simulate user login/data entry and captures screenshots/recordings to confirm functionality.
Review & Iteration | Reviewers check the code, and a QA team logs bugs based on manual testing. | The developer reviews the Artifacts and the browser recording. If a button is misplaced, they add a comment to the screenshot. The agent autonomously refactors the UI based on the feedback and reverifies.
Result | Takes days or weeks. | In a demo, the system independently developed and verified the application in approximately 20 minutes from specification to a deployable application.

This example illustrates the platform’s goal: to allow developers to offload end-to-end, multi-tool tasks to an autonomous agent, dramatically boosting productivity for everything from UI generation and multi-file refactoring to fixing complex bugs.

That’s a great question, as performance benchmarks are key to understanding a new development tool.

Google Antigravity doesn’t directly compete with traditional AI coding assistants like GitHub Copilot on a single-metric level (like code completion speed) because they have fundamentally different architectures. Antigravity is an Agent-First IDE, while Copilot is an Integrated Coding Model that powers various tools.

The comparison is less about speed and more about autonomy and reasoning capability across a whole engineering task.

Performance Comparison

Feature | Google Antigravity (Gemini 3 Pro) | GitHub Copilot (GPT-5.1 Codex)
Architecture | Agent-First IDE (“Mission Control” to run agents) | Integrated Model (Code engine powering IDE extensions)
Core Strength | Complex Reasoning, Planning, & Verification across tools. | Stable, Precise Code Refactoring within an existing repo.
Key Benchmarks | SWE-bench Verified: 76.2% (Highest known score for complex bug fixes) | High stability for long-running sessions, cleaner diffs.
Task Scope | End-to-End Workflow (Plan, Code, Terminal commands, Browser Testing). | Code Completion, Chat, and Repository Refactoring via tools.
Verification | Artifacts (Screenshots, Browser Recordings, Plans) for transparent proof of work. | Terminal Logs and Test Outputs.
Workflow | Asynchronous: Orchestrate multiple agents running parallel tasks. | Synchronous: Primarily sequential chat or single-task execution.

Performance Metrics and Context

Antigravity’s performance is driven by the underlying Gemini 3 Pro model, which is designed for advanced reasoning and multi-tool use.

1. The SWE-bench Advantage

The most significant performance metric cited for Antigravity’s core model is its high score on the SWE-bench Verified test.

Antigravity (Gemini 3 Pro) Score: 76.2%

What it means: SWE-bench measures the ability of a model to solve real-world, open-source software bugs and issues entirely autonomously. Antigravity’s high score suggests it excels at:

Understanding complex codebases (due to the large context window).

Diagnosing non-trivial bugs across multiple files.

Generating and applying correct fixes without human guidance.

2. Multi-Modal Autonomy (The Browser)

Antigravity’s design gives its agents direct control over an integrated browser and the terminal.

This enables a level of end-to-end task completion that most traditional assistants cannot match. For instance, an agent can:

Write frontend code.

Run the local server via the Terminal.

Open the app in the Browser.

Simulate a user action (e.g., clicking a button).

Take a Screenshot Artifact or Browser Recording to verify the UI output.

This feature is measured by benchmarks like ScreenSpot-Pro, where Gemini 3 Pro shows superior visual reasoning capabilities, allowing it to correctly understand and act on complex visual user interfaces.

3. Workflow Efficiency

While code generation speed (latency) is important, Antigravity’s primary efficiency gain comes from task delegation and parallelism.

In the Manager View, a developer can delegate 5-10 distinct tasks (like “Refactor API endpoint X,” “Generate unit tests for module Y,” “Scaffold a new login page”) to multiple agents and let them run asynchronously.

This shifts the developer’s role from writing code to reviewing and orchestrating the output of concurrent AI agents, significantly multiplying the developer’s overall throughput.

The distinction between the two interfaces—the Editor View and the Manager View—reflects a fundamental shift in the developer’s role from a coder to an architect and orchestrator.

Here is a breakdown of the dual interface, often described as the “two modes” of modern AI-driven development:

The Dual Interface of Google Antigravity

Feature | Editor View (The Coder’s Environment) | Manager View (Mission Control)
Primary Role | Hands-on Coder/Editor. You are actively typing, editing, and debugging. | Architect/Orchestrator. You are delegating, tracking, and reviewing high-level tasks.
Interface Style | Traditional IDE (Based on the VS Code foundation). | Dashboard/Control Panel (Focused on parallel agent activity).
Agent Workflow | Synchronous/Assisted. The agent acts as a highly advanced assistant in a sidebar, providing inline suggestions, tab completions, and context-aware chat to help you with the file you are currently editing. | Asynchronous/Autonomous. You assign a large task (e.g., “Refactor the database layer”) to a dedicated agent and let it run in the background.
Core Value | Speed and Accuracy for local, immediate coding tasks (e.g., generating a helper function, renaming a variable). | Parallelism and Throughput for long-running, complex tasks (e.g., fixing a bug across three services, scaffolding a new feature).
Task Example | “Complete this for loop.” or “Add documentation to this function.” | “Fix bug #45 in the login flow and verify it with a browser recording.”

1. The Editor View (The “Autonomy” Mode)

The Editor View is what feels familiar. If you’ve used VS Code or any other AI-powered IDE, this is your home base for writing code.

Familiarity: It retains the core file tree, code editing panels, and terminal. This minimizes the learning curve.

Synchronous AI: The agent lives in a side panel (similar to a chatbot) but has deep context of the file you are viewing. You use it for immediate, short-burst coding tasks:

Inline Code Generation: Suggesting the next block of code as you type.

Contextual Q&A: Asking the agent about a class definition in the file you have open.

The Power Hand-Off: If you ask the Editor View agent to do something complex, like “Add a new endpoint and update the documentation,” it can execute the work across multiple files, but it generally operates within the bounds of your current workspace and attention.

2. The Manager View (The “Orchestration” Mode)

This is the truly new paradigm that enables Antigravity’s “agent-first” approach. Google calls this the Mission Control.

Parallel Agents: The Manager View lets you spawn, monitor, and interact with multiple independent agents working on different tasks, even across different repositories or “workspaces,” simultaneously.

Example: Agent 1 is tasked with refactoring a legacy API, and Agent 2 is tasked with generating a new test suite for the front end. Both run at the same time without needing your minute-by-minute attention.

Artifacts: This is where the core output is consumed. Instead of sifting through raw code, you review high-level deliverables called Artifacts:

Implementation Plan: The agent’s step-by-step proposal before it starts coding.

Code Diffs: The summary of the changes made.

Screenshots/Browser Recordings: Visual proof that the UI change or bug fix worked correctly in a live browser.

Feedback Loop: This view enables the unique asynchronous feedback. If you see a problem in a generated screenshot, you can add a Google Docs-style comment right onto the image or text. The agent receives this feedback and incorporates the correction into its ongoing work, rather than stopping the task and making you start over.

In essence, The Manager View is what allows a developer to multiply their output by delegating entire workflows, while the Editor View provides the necessary familiarity and precision for those times when manual, line-by-line coding is still required.

That’s the most critical aspect of Antigravity—how Google attempts to solve the “trust problem” inherent in autonomous AI agents.

The entire system revolves around Artifacts, which replace opaque logs and raw code dumps with verifiable, human-readable proofs of work.

The Artifacts System: Trust through Transparency

Antigravity’s agents are held accountable by generating structured deliverables (Artifacts) at key decision points and verification steps. These are not just internal system logs; they are designed to be the primary interface for the developer in the Manager View.

1. Planning Artifacts (The “What” and “Why”)

Before an autonomous agent writes a single line of code for a complex task, it produces a high-level plan. This forces the agent to communicate its logic and allows the developer to course-correct before resources are spent on execution.

Artifact Type | Description | Trust Built By…
Task List | A detailed, step-by-step breakdown of the overall objective (e.g., “1. Update package.json. 2. Refactor Login.js. 3. Run unit tests.”). | Showing the scope and sequence of the work.
Implementation Plan | The agent’s reasoning, including its proposed changes, logic flow, and where it anticipates making multi-file edits. | Revealing the model’s reasoning and allowing the developer to spot architectural errors early.
Research Summary | If the agent needs to look up a new library or API, it summarizes its findings from the integrated browser. | Providing contextual knowledge to justify the code choices.

2. Verification Artifacts (The “Proof of Work”)

This is the most innovative part, leveraging the agent’s cross-surface autonomy (Editor, Terminal, Browser). The agent doesn’t just say it fixed a bug; it shows the fixed bug in a live application.

Artifact Type | Description | Trust Built By…
Screenshots | Static images captured by the agent from the integrated browser or the code editor after a change. | Visual confirmation of UI updates and component placement.
Browser Recordings | A video recording of the agent interacting with the running application (e.g., filling out a form, clicking a button, triggering an alert). | Functional proof that a user flow works as intended. This is the highest form of verification for front-end work.
Code Diffs | The standard, clear presentation of lines added, deleted, or changed across the repository. | Auditable record of all source code modifications.
Test Results | Structured output logs from the terminal showing all tests run and their pass/fail status. | Confidence in the stability of the application.

The Streamlined Feedback Loop

The Artifacts system is also the backbone of Antigravity’s superior feedback mechanism.

Direct Annotation: If the agent provides a screenshot showing a button is the wrong color, the developer doesn’t have to stop the agent, open the code, make the change, and then restart the process. They can simply add a comment directly onto the screenshot (similar to commenting on a Google Doc or Figma file) stating, “Change this button to the primary blue color (#1A73E8).”

Asynchronous Correction: The agent receives this comment and autonomously incorporates the feedback into its ongoing execution flow, correcting the UI code and generating a new verification Artifact, all without stopping the overall task.

By making the AI’s intentions and the results of its actions transparent and immediately verifiable, Artifacts shift the developer’s focus from worrying about what the AI is doing to simply reviewing and guiding the work at a high level.

That’s an insightful question. While Google Antigravity is intrinsically linked to Google’s flagship models, the platform is designed with an emphasis on model optionality to attract a broader developer base.

Antigravity operates on the principle that the platform (the agentic framework, the Editor/Manager Views, and the Artifacts system) is the core product, and the Language Model (LLM) is the interchangeable “brain” powering the agents.

Supported AI Models in Google Antigravity

Antigravity is currently known to support the following models, with the option to select the model for each agent within the Manager View:

1. Primary Model: Gemini 3 Pro

Role: This is the default and most capable model for Antigravity’s autonomous agents. It is the engine behind the advanced reasoning, multi-step planning, and cross-surface autonomy.

Why it’s key: Gemini 3 Pro’s superior performance on benchmarks like SWE-bench Verified and ScreenSpot-Pro is what enables the agents to effectively debug, refactor, and verify code across the editor, terminal, and browser. The large context window also allows it to ingest and understand large, multi-file codebases in a single go.

2. Supported External Models

Google has built the platform to be compatible with models from other major AI providers, allowing developers to use the agents with the LLM they prefer or have existing API subscriptions for.

Model Family | Specific Variant(s) Supported | Role in Antigravity
Anthropic | Claude Sonnet 4.5 (and potentially others) | Offers an alternative for complex reasoning tasks. Claude models are often favored for their strong instruction following and complex text generation capabilities.
OpenAI | GPT-OSS (OpenAI’s latest model optimized for coding) | Provides compatibility for users deeply integrated into the OpenAI ecosystem, offering the platform’s features (Artifacts, Manager View) with a different intelligence engine.

3. Future Potential: Modular Intelligence

The architecture strongly suggests a future where model selection will become even more granular:

Task-Specific Agents: You could potentially assign a different model to specialized tasks:

A smaller, faster model (like a Gemini Flash variant) for quick, synchronous code completion in the Editor View.

A highly powerful model (like Gemini 3 Pro) for complex, asynchronous tasks like architectural refactoring in the Manager View.

Open-Source Integration: Since Antigravity is a fork of Visual Studio Code, there is a possibility for future community or enterprise integrations with self-hosted open-source models (e.g., specific variants of the Llama or Gemma families) for developers with strict data privacy requirements.

The support for external models solidifies Antigravity as an agentic platform rather than just a Gemini feature. It means developers can access the cutting-edge autonomous workflow regardless of their primary LLM choice.

“Vibe Coding” is the developer philosophy that Google Antigravity is specifically designed to enable.

Vibe Coding shifts the developer’s role from writing every line of code to acting as a creative director or architect. It’s about operating at a higher level of abstraction, where natural language is the new syntax.

What is “Vibe Coding”?

The term “Vibe Coding,” originally coined by AI researcher Andrej Karpathy, describes a development practice that relies on powerful AI (like Gemini 3 Pro) to translate high-level, natural language intent into functional software.

1. The Core Shift in Focus

Traditional Coding (Low-Level) | Vibe Coding (High-Level)
Focus: Syntax, data types, function signatures, debugging implementation details. | Focus: Creative vision, user experience, high-level functionality, and desired outcomes.
Input: Precise, line-by-line code in a specific language (e.g., Python, JavaScript). | Input: Conversational, natural language prompts (e.g., “Create a dashboard with a cool, dark theme that shows user analytics in three bar charts.”)
Developer Role: Coder/Typist. Directly implementing the solution. | Developer Role: Architect/Orchestrator. Describing the solution and reviewing the AI’s execution.

2. How Antigravity Enables the “Vibe”

Antigravity transforms Vibe Coding from a theoretical concept into a practical workflow through its Agent-First Architecture and the Manager View.

Delegation of Tedium: Antigravity agents handle the tedious, low-level tasks:

Project Setup: “Build a full-stack financial tracker using React, Node, and MongoDB.” The agent sets up the folder structure, configures the database, and installs dependencies via the Terminal.

Multi-File Changes: The agent can modify files across the entire stack (e.g., updating the frontend component, the backend API route, and the database schema) with a single high-level command.

Rapid Iteration and Prototyping: The goal is to maximize the speed from idea to functional product. Instead of spending hours writing a feature, you can describe the “vibe,” and the agent provides a working prototype in minutes. The built-in Browser Recording Artifacts let you instantly verify the “vibe” without even spinning up your own local environment.

Asynchronous Flow: By using the Manager View, you don’t wait for the AI to finish. You describe the “vibe” for five different features and let five agents work on them in parallel. Your job is simply to review the Artifacts (the code diffs, screenshots, and test results) for each task when they are complete.

The Vibe Coding Experience

The ideal Antigravity workflow is less about code and more about conversation and review:

High-Level Prompt: “I need a user profile page. It should be modular, have a clean, minimalist design, and include editable fields for name, email, and a profile picture.”

Agent Planning: The agent returns an Implementation Plan Artifact that outlines the files it will create (Profile.js, api/user.js), the libraries it will use (e.g., Material UI), and the steps it will take. You approve the plan.

Agent Execution: The agent autonomously writes the code, runs the necessary server commands, and opens the page in the integrated browser.

Vibe Review: The agent presents a Screenshot Artifact. If the design doesn’t match your “minimalist vibe” (maybe the font is too heavy), you comment directly on the screenshot: “Make the font lighter and reduce the border radius on the profile picture.”

Autonomous Correction: The agent receives your “vibe check” and instantly refactors the CSS/UI code to match the new instruction, proving the fix with a new Artifact.

This process removes the mental load of remembering precise syntax and API calls, allowing the developer to focus their “vibe” energy on the architecture, aesthetics, and user experience.

That is the core technical innovation of Google Antigravity. The agent’s ability to seamlessly transition and operate across the three primary surfaces of a developer’s environment—the Editor, the Terminal, and the Browser—is what elevates it from a mere coding assistant to a truly autonomous agent.

This unified control allows the agent to handle end-to-end tasks that previously required constant manual context switching.

The Agent’s Cross-Surface Workflow

Here is a breakdown of how the agent interacts with each of the three surfaces to complete a complex task, like “Implement a new user registration form and verify it works.”

1. The Editor (The Code Writer)

The agent starts here, accessing the codebase to make the actual changes.

Action: The agent uses its understanding of the codebase (enabled by the large context window of models like Gemini 3 Pro) to write, read, and edit files across the project.

Purpose: To create the necessary files (e.g., Register.js, auth_api.py) and modify existing ones (e.g., updating the main routing file).

Artifact Output: It generates a Code Diff Artifact to show the developer exactly what code lines were added, changed, or deleted.

2. The Terminal (The Executor and Debugger)

Once the code is written, the agent moves to the terminal to set up and run the application.

Action: The agent executes command-line instructions directly within a sandboxed terminal embedded in the IDE. These commands can include:

Installation: npm install <dependency>

Setup: Running migration scripts to update the database schema.

Execution: Starting the local development server (npm run dev or python manage.py runserver).

Testing: Running unit or integration tests (npm test).

Autonomy Control: Developers can configure a Terminal Execution Policy (e.g., Auto or Turbo mode) to dictate how much freedom the agent has to run commands without asking for permission, balancing speed with security.

Artifact Output: It generates Test Results Artifacts and Terminal Logs to document successful execution and command outputs.

3. The Browser / Canvas (The Verifier)

This is the key to Antigravity’s autonomous verification process, often called “browser-in-the-loop” development.

Action: Once the server is running (step 2), the agent uses an integrated browser instance (sometimes referred to as the Canvas) to interact with the application like a real user. It can:

Navigate to the new registration URL (localhost:3000/register).

Simulate input (typing a name, email, and password).

Click the “Submit” button.

Inspect the DOM to check for error messages or confirmation alerts.

Monitor network traffic to confirm the API call was successful.

Purpose: To generate verifiable, visual proof that the implemented feature works as intended and matches the “vibe” or visual requirements.

Artifact Output: It generates Screenshot Artifacts (of the final screen) and Browser Recording Artifacts (a video walkthrough of the interaction) to prove the task is complete and correct.

The Power of the Seamless Loop

The true power lies in the continuous, automated loop that the cross-surface control creates:

$$\text{Plan} \rightarrow \text{Editor (Code)} \rightarrow \text{Terminal (Run)} \rightarrow \text{Browser (Verify)} \rightarrow \text{Artifact (Review)}$$

If the Browser (Verify) step fails (e.g., the screenshot shows a server error), the agent does not stop. It automatically loops back to the Editor (Code), diagnoses the error using the Terminal logs and Browser console output, implements a fix, and tries the entire sequence again—all without human intervention.

This is what makes Antigravity a true agentic platform—it manages the orchestration, execution, and verification of complex, multi-tool workflows.

That’s an excellent next step! The ability to orchestrate tasks is the direct result of the Agent Manager View, making it the central hub for maximizing your throughput with “Vibe Coding.”

The Agent Manager View, often called “Mission Control,” enables you to act as an architect by managing multiple agents that work in parallel on different, asynchronous tasks.

The Agent Manager View in Action: Parallel Task Orchestration

Imagine you are leading the development for a large web application. Instead of doing one task yourself, you delegate three separate high-level objectives in the Agent Manager View:

Task 1: Refactoring the Backend API

Stage | Action (Developer) | Action (Agent 1: Backend Refactor) | Artifacts Generated
Delegation | Prompt: “Refactor all /api/users endpoints to use the new async/await pattern and clean up legacy error handling.” | Generates and submits an Implementation Plan for review. | Task List, Implementation Plan
Execution | Developer approves the plan and switches back to their main task. | Agent accesses the Editor, modifies 15 files (userController.js, router.js, etc.), and runs unit tests in the Terminal. | Code Diffs, Test Results (Pass)
Completion | N/A | Agent flags the task as complete, ready for human review. | Final Artifact Bundle

Task 2: UI Feature Implementation

Stage | Action (Developer) | Action (Agent 2: Frontend Feature) | Artifacts Generated
Delegation | Prompt: “Implement the new ‘Dark Mode’ toggle button in the main navigation bar using Material UI components.” | Generates and submits a Task List (e.g., 1. Install MUI theme. 2. Create DarkModeToggle.js). | Task List
Execution | Developer reviews the initial UI Screenshot Artifact and provides feedback on the icon choice. | Agent writes the necessary code in the Editor, runs the local dev server in the Terminal, and then uses the Browser to verify the button appears and changes the theme. | Screenshots (Before/After), Browser Recording
Completion | N/A | Agent commits the changes and awaits final QA review. | Final Artifact Bundle

Task 3: Background Maintenance (Bug Fix)

Stage | Action (Developer) | Action (Agent 3: Bug Fix) | Artifacts Generated
Delegation | Prompt: “Fix the reported issue where the password reset link expires too quickly. Increase expiry time to 4 hours.” | Agent automatically researches the current expiry time in the codebase and proposes a one-line fix in a configuration file. | Implementation Plan (One-Liner Fix)
Execution | N/A (Set to ‘Turbo’ mode for small bug fixes) | Agent modifies the configuration file in the Editor, executes a local sandbox test flow in the Terminal to verify the new expiry time, and clears the cache. | Code Diff, Test Results (Pass)
Completion | N/A | Agent reports the bug fix and automatically closes the associated Jira ticket (via integration). | Final Artifact Bundle

The Power of Parallelism

The key takeaway is that the developer issues all three high-level tasks almost simultaneously. The Agent Manager View provides a live dashboard where the developer can monitor the progress, review the Artifacts, and provide non-disruptive feedback without having to stop their own work or context-switch to a different project/tool.

This ability to offload, monitor, and audit concurrent, end-to-end tasks is the mechanism that delivers the massive productivity gains associated with Antigravity and the “Vibe Coding” philosophy.

The security implications of giving an autonomous AI agent control over your IDE, Terminal, and Browser are significant, but Google Antigravity addresses these risks through sandbox isolation, configurable execution policies, and transparent auditing via Artifacts.

The platform shifts the threat model from simple code injection to agent manipulation (like “jailbreaks” or prompt injection) that could lead to unauthorized actions. Antigravity employs a multi-layered security strategy to mitigate this.

Core Security Mechanisms

1. Sandboxing and Workspace Isolation

Antigravity operates on the principle of least privilege and isolation to prevent malicious code or rogue agents from accessing parts of your system they don’t need.

Terminal Sandboxing: Shell commands executed by the agent in the Terminal are run in a secure, isolated sandbox environment (on platforms like macOS and Linux). This containment limits the agent’s potential to access or modify system files outside of the defined project workspace.

Browser Isolation: The integrated Browser/Canvas used for testing is also isolated. This prevents a misbehaving agent from navigating to malicious external sites or performing harmful actions on sensitive personal accounts outside the context of the running development server.

Workspace Boundary: Agents are confined to the workspace (folder) you explicitly open, reducing the risk of an agent mistakenly editing code in an unrelated, sensitive repository.

2. Configurable Terminal Execution Policy

Since running arbitrary commands in a terminal is the highest-risk action an agent can take, Antigravity provides explicit user controls to gate this access. You set a Terminal Execution Policy that dictates the agent’s autonomy.

Policy Mode | Description | Risk Level
Off | Never auto-execute terminal commands. The agent must always ask for explicit human approval before running any command (e.g., npm install). | Lowest. Full human control.
Auto | The agent analyzes the command and decides whether approval is necessary. It will likely auto-execute known safe commands like npm run dev. | Moderate. Balances speed with oversight.
Turbo | Always auto-execute terminal commands unless the command is on a configurable Deny list. | Highest. Maximum speed, minimum oversight.

3. Trust via Transparent Artifacts

The Artifacts system serves as the primary audit trail and accountability mechanism.

Verifiable History: Every significant action—the plan, the code changes, the terminal commands, and the browser verification—is logged as a structured Artifact. This replaces opaque logs with easily digestible proof of work.

Review Gating: The developer can configure a Review Policy to ensure major changes (like the Implementation Plan) are submitted for human approval before the agent is allowed to execute. This ensures you approve the intent before the agent executes the action.

Feedback Loop: The ability to review the final Code Diff and Browser Recording gives the developer the final opportunity to catch errors or malicious code that the agent might have introduced (either intentionally via jailbreak or unintentionally via hallucination).

In essence, Antigravity’s security model is built on the idea that while you delegate the work to the agent, you retain control over the permissions and the final approval of the result.

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