AskUI - Enterprise Computer Use Agent Infrastructure

TLDR

A vision agent is an AI model that uses computer vision to interact with graphical user interfaces (GUIs), automating tasks by "seeing" and controlling the screen like a human user. This technology offers significant advantages over traditional selector-based automation tools, especially for testing legacy systems, applications without stable DOM elements, and cross-platform automation scenarios.

Introduction

A vision agent is an AI-powered automation tool that combines computer vision, optical character recognition (OCR), and large language models (LLMs) to interact with any computer interface through visual perception. Unlike traditional automation frameworks that rely on code-level selectors, vision agents operate at the operating system level, making them platform-agnostic and capable of automating Windows, macOS, Linux, web applications, and mobile apps without requiring access to source code or DOM structures.

Vision Agent vs Traditional Automation: Understanding the Difference

Traditional test automation tools rely on DOM selectors or object identifiers to interact with elements. Vision agents take a fundamentally different approach by using visual recognition. Here's a detailed comparison:

Feature	Vision Agent	Traditional Automation Tools
Element Identification	Computer vision, OCR, image recognition	DOM selectors (id, class, XPath, CSS selectors)
Resilience to Code Changes	High - unaffected by refactoring if UI remains visually consistent	Low - breaks when selectors change
Application Support	Any GUI application (desktop, web, mobile, legacy systems)	Limited to specific platforms with accessible object models
Setup Complexity	Minimal - no selector maintenance required	Requires selector strategy and maintenance
Use Cases	Black-box testing, legacy system automation, cross-platform testing	Testing with stable identifiers and API access

Key Applications and Use Cases

1. End-to-End (E2E) Test Automation

Vision agents excel at end-to-end testing scenarios where traditional tools struggle:

Legacy System Testing: Automate SAP GUI, mainframe terminals, and desktop applications
Canvas and WebGL Testing: Test graphics-heavy applications, games, and design tools
Cross-Browser Testing: Consistent automation across different browsers without selector adjustments
Visual Regression Testing: Detect UI changes that affect user experience

2. Robotic Process Automation (RPA)

Vision agents enable intelligent RPA solutions for:

Data Entry Automation: Extract data from PDFs, images, or legacy systems
Cross-Application Workflows: Automate workflows spanning multiple applications
Document Processing: Read and process scanned documents, invoices, and forms
Remote Desktop Automation: Automate tasks on virtual machines and remote sessions

How Vision Agents Work: Technical Architecture

Vision agents typically consist of three core components:

Computer Vision Engine: Captures and analyzes screen content in real-time
AI Recognition Model: Identifies UI elements, text, and patterns using machine learning
Action Controller: Executes mouse clicks, keyboard inputs, and system commands

Screen Capture → Visual Analysis → Element Recognition → Action Execution

Getting Started: Build Your First Vision Agent with Python

This practical tutorial demonstrates how to create a vision agent using Python for GUI automation.

Prerequisites

Python 3.8 or higher installed on your system
VS Code or any Python IDE
Windows, macOS, or Linux operating system

Step 1: Installation and Setup

Create a Python Project Directory:

mkdir vision-agent-demo
cd vision-agent-demo

# Create virtual environment
python -m venv venv

# Activate virtual environment
# On macOS/Linux:
source venv/bin/activate
# On Windows:
venv\activate.bat

# Install AskUI
pip install askui

Set up credentials:

export ASKUI_WORKSPACE_ID="your-workspace-id"
export ASKUI_TOKEN="your-token"

Step 2: Write Your First Vision Agent Code

Create a file named vision_agent_demo.py with the following code:

from askui import VisionAgent
from askui import locators as loc
import logging

def automate_with_vision():
    """
    Demonstrates vision-based GUI automation using visual recognition
    and computer vision for element detection.
    """
    
    # Initialize the vision agent
    with VisionAgent(log_level=logging.INFO) as agent:
        try:
            # Example 1: Click on application using text recognition
            agent.click(loc.Text("Chrome"))
            
            # Example 2: Type in the active field
            agent.type("https://example.com")
            agent.keyboard("enter")
            
            # Example 3: Fill form using visual context
            # The agent finds input fields near label text
            agent.click(loc.Text("Email"))
            agent.type("user@example.com")
            
            agent.click(loc.Text("Password"))
            agent.type("securepassword")
            
            # Example 4: Click button using OCR
            agent.click(loc.Text("Sign In"))
            
            # Example 5: Use visual relationships
            agent.click(
                loc.Button()
                .below_of(loc.Text("Terms and Conditions"))
            )
            
            print("✅ Automation completed successfully")
            
        except Exception as e:
            print(f"❌ Automation error: {e}")

if __name__ == "__main__":
    automate_with_vision()

Step 3: Execute the Vision Agent

Run your code to see the vision agent in action:

python vision_agent_demo.py

The agent will visually identify and interact with UI elements on your screen, demonstrating the power of computer vision-based automation.

Best Practices for Vision Agent Development

1. Optimize Visual Selectors

Use unique, visible text when possible
Combine multiple visual attributes for precision
Consider screen resolution and scaling factors

2. Handle Dynamic Content

# Implement wait strategies
agent.wait(2)  # Wait 2 seconds

# Use conditional checks
if agent.get("Is loading complete?"):
    agent.click(loc.Text("Continue"))

# Add retry logic
for _ in range(3):
    try:
        agent.click(loc.Text("Submit"))
        break
    except:
        agent.wait(1)

3. Maintain Visual Stability

Ensure consistent lighting for image recognition
Account for theme changes (light/dark mode)
Test across different screen resolutions

Common Use Cases by Industry

Financial Services

SAP Banking Automation: Automate transaction processing in SAP GUI
Legacy System Integration: Connect modern apps with mainframe systems
Compliance Testing: Validate UI compliance across trading platforms

Healthcare

EMR System Testing: Automate testing of electronic medical records
Cross-Platform Validation: Test medical devices with proprietary interfaces
Data Migration: Extract data from legacy healthcare systems

Manufacturing

ERP Testing: Automate SAP, Oracle, and custom ERP testing
Quality Assurance: Visual inspection of HMI/SCADA systems
Process Automation: Automate repetitive data entry tasks

Advantages of Vision Agents

No Source Code Required: Perfect for black-box testing and third-party applications
Platform Independent: One solution for web, desktop, and mobile automation
Maintenance Efficiency: No selector updates needed when code changes
Human-Like Interaction: Mimics actual user behavior for realistic testing
Legacy System Support: Automate applications that traditional tools cannot access

Limitations and Considerations

Processing Speed: Visual recognition may be slower than direct DOM manipulation
Resource Requirements: Requires more CPU/GPU resources for image processing
Visual Dependencies: Sensitive to resolution changes and visual themes
Initial Setup: May require training for complex visual patterns

Frequently Asked Questions

What is a vision agent in AI? A vision agent is an AI system that uses computer vision and machine learning to interact with graphical user interfaces by "seeing" the screen like a human user, rather than relying on code-level access.

How do vision agents differ from Selenium? While Selenium requires DOM selectors and works only with web browsers, vision agents use visual recognition to automate any application with a GUI, including desktop software, legacy systems, and applications without accessible DOM.

Can vision agents work with legacy systems? Yes, vision agents excel at automating legacy systems like SAP GUI, mainframe terminals, and older desktop applications that traditional automation tools cannot access.

What programming languages support vision agents? Vision agents can be implemented in Python, with libraries like AskUI providing comprehensive Python support for vision-based automation.

Do vision agents require access to source code? No, vision agents operate through visual recognition and do not require access to application source code, APIs, or DOM structures, making them ideal for black-box testing.

Conclusion

Vision agents represent a paradigm shift in test automation and RPA, offering a robust solution for scenarios where traditional tools fall short. By leveraging computer vision and AI, they provide platform-agnostic automation capabilities that are resilient to code changes and capable of handling complex visual interfaces. Whether you're automating legacy systems, conducting cross-platform testing, or implementing intelligent RPA workflows, vision agents offer a powerful and flexible approach to GUI automation.

What Is a Vision Agent? A Practical Guide for Developers