Gemini 2.5 Computer Use Model: The Revolution That Teaches AI to “Use Computers” Is Here

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As you read this, you might be tired of repetitive web operations or frustrated with tedious UI testing. Now, there’s a new solution to these challenges.

Ten years ago, we dreamed of AI assistants that could handle repetitive computer tasks. Today, Google has turned that dream into reality. Based on Gemini 2.5 Pro, the Gemini 2.5 Computer Use model doesn’t just understand your instructions—it actually “sees” the screen and performs clicks, typing, and scrolling like a human, accomplishing tasks that were once strictly manual.

From Imagination to Reality: When AI Truly “Sees” and “Operates” Graphical Interfaces

While traditional AI interacts with software through structured APIs, numerous digital tasks still require direct engagement with graphical user interfaces. Think about filling out tedious expense forms, comparing prices across multiple websites, or performing repetitive UI testing—these seemingly simple tasks consume valuable time.

Officially released by Google DeepMind on October 7th, the Gemini 2.5 Computer Use model addresses this exact pain point. Unlike traditional APIs that require developers to predefine every action, it analyzes screen captures and autonomously decides how to interact with interfaces.

This isn’t just another AI model—it’s an intelligent agent that genuinely “uses computers.”

Demystifying the Core Technology: How Computer Use Works

To understand this technological breakthrough, we need to examine its core operational mechanism. Similar to how humans interact with computers, Gemini 2.5 Computer Use follows an intuitive cyclical process:

1. Observation: The model receives a screenshot of the current screen
2. Analysis: It processes user instructions and screen content to determine the next action
3. Action: Generates specific UI operations (clicks, typing, scrolling, etc.)
4. Feedback: Captures the new state after action execution and restarts the cycle

The brilliance of this approach is that the model doesn’t need prior knowledge of the target website’s structure—it understands interface elements and their functions through visual analysis, just like humans do.

Key Code: Understanding the Model’s “Action Language”

The model expresses its operational intentions through function calls. Here are typical examples:

# Click operation
{"name": "click_at", "args": {"x": 371, "y": 470}}

# Text input
{"name": "type_text_at", "args": {"x": 400, "y": 250, "text": "search query", "press_enter": true}}

# Page scrolling
{"name": "scroll_document", "args": {"direction": "down"}}

These coordinates are normalized to a 1000×1000 grid, allowing the model to automatically adapt to different screen resolutions. This design ensures both flexibility and operational precision.

Hands-On Implementation: Building Your First AI Assistant from Scratch

Enough theory—let’s build an actual Computer Use agent. I’ll guide you through creating an AI assistant that automates web operations.

Environment Setup: Laying the Foundation

First, set up your development environment:

# Clone the official example code
git clone https://github.com/google/computer-use-preview.git
cd computer-use-preview

# Create Python virtual environment
python3 -m venv .venv
source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Install Playwright and browser
playwright install-deps chrome
playwright install chrome

Authentication: Gaining API Access

Next, configure API access. Gemini 2.5 Computer Use supports two authentication methods:

Option A: Using Gemini Developer API

export GEMINI_API_KEY="YOUR_GEMINI_API_KEY"

Option B: Using Vertex AI (for enterprise users)

export USE_VERTEXAI=true
export VERTEXAI_PROJECT="YOUR_PROJECT_ID"
export VERTEXAI_LOCATION="YOUR_LOCATION"

Consider adding these configurations to your virtual environment activation script to avoid reconfiguration.

Core Implementation: Building the Agent Loop

Now for the most exciting part—writing the core logic of your AI agent. The following code demonstrates a complete agent loop implementation:

from playwright.sync_api import sync_playwright
from google import genai
from google.genai import types

# Initialize client and browser
client = genai.Client()
playwright = sync_playwright().start()
browser = playwright.chromium.launch(headless=False)
context = browser.new_context(viewport={"width": 1440, "height": 900})
page = context.new_page()

try:
    # Navigate to starting page
    page.goto("https://www.google.com")
    
    # Configure Computer Use tool
    config = types.GenerateContentConfig(
        tools=[types.Tool(computer_use=types.ComputerUse(
            environment=types.Environment.ENVIRONMENT_BROWSER
        ))],
        thinking_config=types.ThinkingConfig(include_thoughts=True),
    )
    
    # Initialize conversation history
    initial_screenshot = page.screenshot(type="png")
    user_prompt = "Search for Gemini API pricing information"
    
    contents = [
        types.Content(role="user", parts=[
            types.Part(text=user_prompt),
            types.Part.from_bytes(data=initial_screenshot, mime_type='image/png')
        ])
    ]
    
    # Agent loop - maximum 5 interaction turns
    for turn in range(5):
        print(f"Turn {turn+1} thinking...")
        response = client.models.generate_content(
            model='gemini-2.5-computer-use-preview-10-2025',
            contents=contents,
            config=config,
        )
        
        candidate = response.candidates[0]
        contents.append(candidate.content)
        
        # Check if actions need execution
        has_actions = any(part.function_call for part in candidate.content.parts)
        if not has_actions:
            print("Task completed!")
            break
            
        # Execute model-suggested actions
        execute_function_calls(candidate, page, 1440, 900)
        
        # Capture new state and continue
        function_responses = get_function_responses(page, [])
        contents.append(
            types.Content(role="user", 
                         parts=[types.Part(function_response=fr) for fr in function_responses])
        )
        
finally:
    browser.close()
    playwright.stop()

This simple example demonstrates how an AI agent completes complex tasks through multi-turn interactions. In practice, you’ll observe the model locating search fields, entering queries, and clicking search buttons—exactly mimicking human operation patterns.

Beyond Browsers: Extending to Mobile and Other Platforms

While Gemini 2.5 Computer Use is primarily optimized for browsers, its architecture allows extension to other platforms. Through custom functions, you can enable the model to operate mobile apps and even desktop software:

def open_app(app_name: str, intent: str = None) -> dict:
    """Opens the specified application"""
    return {"status": "requested_open", "app_name": app_name, "intent": intent}

def long_press_at(x: int, y: int) -> dict:
    """Long press at specified coordinates"""
    return {"x": x, "y": y}

def go_home() -> dict:
    """Returns to home screen"""
    return {"status": "home_requested"}

By combining these custom functions with the Computer Use tool, you can build truly cross-platform automation assistants.

Safety First: Responsible AI Assistant Deployment

While empowering AI to directly operate computers sounds powerful, it introduces new security considerations. Google has implemented comprehensive safeguards:

Built-in Security Mechanisms

The model incorporates multiple security layers:

• Step-by-step safety checks: Security services evaluate potential risks before each action execution
• User confirmation mechanism: For sensitive operations (purchases, messaging), the model requires explicit user confirmation
• Configurable system instructions: Developers can define additional safety rules

Security Practice Code Example

When the model encounters sensitive operations, your code should handle them appropriately:

def get_safety_confirmation(safety_decision):
    """Handles safety decisions requiring user confirmation"""
    print("⚠️  Security reminder: This action requires confirmation")
    print(safety_decision["explanation"])
    
    decision = input("Continue? [Y]es/[N]o: ")
    if decision.lower() in ("n", "no"):
        return "TERMINATE"
    return "CONTINUE"

# Check safety decisions before executing actions
if 'safety_decision' in function_call.args:
    decision = get_safety_confirmation(function_call.args['safety_decision'])
    if decision == "TERMINATE":
        break
    # Record user confirmation
    extra_fields["safety_acknowledgement"] = "true"

Performance Testing: More Than Theoretical Advantages

You might wonder how this model performs in real-world tasks. According to evaluations by Google and Browserbase, Gemini 2.5 Computer Use delivers exceptional results across multiple benchmarks:

• Significantly higher success rates in web operation tasks compared to similar models
• Approximately 50% reduction in response latency, enabling near real-time interactions
• Maintains high consistency in complex multi-step tasks

Early adopters are already seeing practical value. For instance, Google’s payments platform team uses the model to handle fragile UI tests, successfully recovering over 60% of previously failing test cases.

Practical Scenarios: Applications from Simple to Complex

Scenario 1: Automated Data Collection

“Help me collect prices and specifications for the top 5 smart refrigerators from e-commerce sites and organize them into a table”

The model automatically:

1. Navigates to target websites
2. Applies filters (price, specifications, etc.)
3. Extracts product information
4. Organizes data into structured format

Scenario 2: Cross-System Workflows

“Log into the company CRM, export last week’s customer feedback to Excel, then email it to the team”

This complex task involving multiple systems is handled step-by-step, just like a human employee.

Scenario 3: UI Regression Testing

“Check if the new version breaks existing user workflows”

The model executes complete user journey testing, adapting more flexibly to UI changes than traditional scripts.

Quick Start Guide

Want to try it immediately? The simplest approach is through Browserbase’s demo environment:

1. Visit gemini.browserbase.com
2. Enter the task you want the AI to complete
3. Observe how the model operates the browser step by step

For developers, we recommend starting with the official example code:

# Run sample task
python main.py --query="Search for Hello World on Google" --env="playwright"

Future Outlook: A New Era for AI Agents

The release of Gemini 2.5 Computer Use marks a significant milestone in AI agent development. This represents not just technological progress, but a fundamental shift in human-computer interaction. Imagine:

• Personal digital assistants that genuinely help with daily computer tasks
• Enterprise automation reaching unprecedented levels of flexibility and intelligence
• Software testing entering an AI-driven new era

As Google DeepMind states, this is just the beginning. As model capabilities evolve, we’ll likely see AI agents taking on increasingly complex scenarios.

Frequently Asked Questions

Q: How does Gemini 2.5 Computer Use differ from traditional RPA (Robotic Process Automation)?
A: Traditional RPA relies on predefined rules and interface element positioning, while Computer Use dynamically adapts to interface changes through visual understanding, eliminating the need to pre-program every step.

Q: How does the model handle dynamically loaded content?
A: The model naturally handles dynamic content through its cyclical “observe-act” process. If content hasn’t loaded yet, it will wait or perform actions that trigger loading.

Q: Does the model require specialized training for each website?
A: No. The model possesses general interface understanding capabilities and can be directly applied to unfamiliar websites and applications.

Q: How is security ensured? Could the model perform dangerous operations?
A: The model incorporates multiple built-in security layers, including real-time safety evaluation and user confirmation processes. We recommend testing and running agents in sandboxed environments.

Q: Which browsers and platforms are currently supported?
A: Primarily optimized for Chrome browser, but extendable to other browsers through Playwright. Mobile support is still being refined.

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The value of technology lies not in its sophistication, but in how it empowers ordinary people to accomplish extraordinary things. Gemini 2.5 Computer Use represents exactly this kind of technology—it’s not designed to replace humans, but to enhance our capabilities, freeing us from repetitive labor so we can focus on work that truly requires creativity and wisdom.

Now, it’s time to put your AI assistant to work.