Computer Use Agents

Build AI agents that interact with computers like humans do - viewing screens, moving cursors, clicking buttons, and typing text. Covers Anthropic's Computer Use, OpenAI's Operator/CUA, and open-source alternatives. Critical focus on sandboxing, security, and handling the unique challenges of vision-based control.

Patterns

Perception-Reasoning-Action Loop

The fundamental architecture of computer use agents: observe screen, reason about next action, execute action, repeat. This loop integrates vision models with action execution through an iterative pipeline.

Key components:

1. PERCEPTION: Screenshot captures current screen state
2. REASONING: Vision-language model analyzes and plans
3. ACTION: Execute mouse/keyboard operations
4. FEEDBACK: Observe result, continue or correct

Critical insight: Vision agents are completely still during "thinking" phase (1-5 seconds), creating a detectable pause pattern.

When to use: Building any computer use agent from scratch,Integrating vision models with desktop control,Understanding agent behavior patterns

from anthropic import Anthropic from PIL import Image import base64 import pyautogui import time

class ComputerUseAgent: """ Perception-Reasoning-Action loop implementation. Based on Anthropic Computer Use patterns. """

def __init__(self, client: Anthropic, model: str = "claude-sonnet-4-20250514"): self.client = client self.model = model self.max_steps = 50 # Prevent runaway loops self.action_delay = 0.5 # Seconds between actions

def capture_screenshot(self) -> str: """Capture screen and return base64 encoded image.""" screenshot = pyautogui.screenshot()

Resize for token efficiency (1280x800 is good balance)

screenshot = screenshot.resize((1280, 800), Image.LANCZOS)

import io buffer = io.BytesIO() screenshot.save(buffer, format="PNG") return base64.b64encode(buffer.getvalue()).decode()

def execute_action(self, action: dict) -> dict: """Execute mouse/keyboard action on the computer.""" action_type = action.get("type")

if action_type == "click": x, y = action["x"], action["y"] button = action.get("button", "left") pyautogui.click(x, y, button=button) return {"success": True, "action": f"clicked at ({x}, {y})"}

elif action_type == "type": text = action["text"] pyautogui.typewrite(text, interval=0.02) return {"success": True, "action": f"typed {len(text)} chars"}

elif action_type == "key": key = action["key"] pyautogui.press(key) return {"success": True, "action": f"pressed {key}"}

elif action_type == "scroll": direction = action.get("direction", "down") amount = action.get("amount", 3) scroll = -amount if direction == "down" else amount pyautogui.scroll(scroll) return {"success": True, "action": f"scrolled {direction}"}

elif action_type == "move": x, y = action["x"], action["y"] pyautogui.moveTo(x, y) return {"success": True, "action": f"moved to ({x}, {y})"}

else: return {"success": False, "error": f"Unknown action: {action_type}"}

def run(self, task: str) -> dict: """ Run perception-reasoning-action loop until task complete.

The loop:

1. Screenshot current state
2. Send to vision model with task context
3. Parse action from response
4. Execute action
5. Repeat until done or max steps

""" messages = [] step_count = 0

system_prompt = """You are a computer use agent. You can see the screen and control mouse/keyboard.

Available actions (respond with JSON):

• {"type": "click", "x": 100, "y": 200, "button": "left"}
• {"type": "type", "text": "hello world"}
• {"type": "key", "key": "enter"}
• {"type": "scroll", "direction": "down", "amount": 3}
• {"type": "done", "result": "task completed successfully"}

Always respond with ONLY a JSON action object. Be precise with coordinates - click exactly where needed. If you see an error, try to recover. """

while step_count < self.max_steps: step_count += 1

1. PERCEPTION: Capture current screen

screenshot_b64 = self.capture_screenshot()

2. REASONING: Send to vision model

user_content = [ {"type": "text", "text": f"Task: {task}\n\nStep {step_count}. What action should I take?"}, {"type": "image", "source": { "type": "base64", "media_type": "image/png", "data": screenshot_b64 }} ]

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