rootcastleco Computer Use Agents
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:
- PERCEPTION: Screenshot captures current screen state
- REASONING: Vision-language model analyzes and plans
- ACTION: Execute mouse/keyboard operations
- 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 {dir
Sandboxed Environment Pattern
Computer use agents MUST run in isolated, sandboxed environments. Never give agents direct access to your main system - the security risks are too high. Use Docker containers with virtual desktops.
Key isolation requirements:
- NETWORK: Restrict to necessary endpoints only
- FILESYSTEM: Read-only or scoped to temp directories
- CREDENTIALS: No access to host credentials
- SYSCALLS: Filter dangerous system calls
- RESOURCES: Limit CPU, memory, time
The goal is "blast radius minimization" - if the agent goes wrong, damage is contained to the sandbox.
When to use: ['Deploying any computer use agent', 'Testing agent behavior safely', 'Running untrusted automation tasks']
# Dockerfile for sandboxed computer use environment
# Based on Anthropic's reference implementation pattern
FROM ubuntu:22.04
# Install desktop environment
RUN apt-get update && apt-get install -y \
xvfb \
x11vnc \
fluxbox \
xterm \
firefox \
python3 \
python3-pip \
supervisor
# Security: Create non-root user
RUN useradd -m -s /bin/bash agent && \
mkdir -p /home/agent/.vnc
# Install Python dependencies
COPY requirements.txt /tmp/
RUN pip3 install -r /tmp/requirements.txt
# Security: Drop capabilities
RUN apt-get install -y --no-install-recommends libcap2-bin && \
setcap -r /usr/bin/python3 || true
# Copy agent code
COPY --chown=agent:agent . /app
WORKDIR /app
# Supervisor config for virtual display + VNC
COPY supervisord.conf /etc/supervisor/conf.d/
# Expose VNC port only (not desktop directly)
EXPOSE 5900
# Run as non-root
USER agent
CMD ["/usr/bin/supervisord", "-c", "/etc/supervisor/conf.d/supervisord.conf"]
---
# docker-compose.yml with security constraints
version: '3.8'
services:
computer-use-agent:
build: .
ports:
- "5900:5900" # VNC for observation
- "8080:8080" # API for control
# Security constraints
security_opt:
- no-new-privileges:true
- seccomp:seccomp-profile.json
# Resource limits
deploy:
resources:
limits:
cpus: '2'
memory: 4G
reservations:
cpus: '0.5'
memory: 1G
# Network isolation
networks:
- agent-network
# No access to host filesystem
volumes:
- agent-tmp:/tmp
# Read-only root filesystem
read_only: true
tmpfs:
- /run
- /var/run
# Environment
environment:
- DISPLAY=:99
- NO_PROXY=localhost
networks:
agent-network:
driver: bridge
internal: true # No internet by default
volumes:
agent-tmp:
---
# Python wrapper with additional runtime sandboxing
import subprocess
import os
from dataclasses im
Anthropic Computer Use Implementation
Official implementation pattern using Claude's computer use capability. Claude 3.5 Sonnet was the first frontier model to offer computer use. Claude Opus 4.5 is now the "best model in the world for computer use."
Key capabilities:
- screenshot: Capture current screen state
- mouse: Click, move, drag operations
- keyboard: Type text, press keys
- bash: Run shell commands
- text_editor: View and edit files
Tool versions:
- computer_20251124 (Opus 4.5): Adds zoom action for detailed inspection
- computer_20250124 (All other models): Standard capabilities
Critical limitation: "Some UI elements (like dropdowns and scrollbars) might be tricky for Claude to manipulate" - Anthropic docs
When to use: ['Building production computer use agents', 'Need highest quality vision understanding', 'Full desktop control (not just browser)']
from anthropic import Anthropic
from anthropic.types.beta import (
BetaToolComputerUse20241022,
BetaToolBash20241022,
BetaToolTextEditor20241022,
)
import subprocess
import base64
from PIL import Image
import io
class AnthropicComputerUse:
"""
Official Anthropic Computer Use implementation.
Requires:
- Docker container with virtual display
- VNC for viewing agent actions
- Proper tool implementations
"""
def __init__(self):
self.client = Anthropic()
self.model = "claude-sonnet-4-20250514" # Best for computer use
self.screen_size = (1280, 800)
def get_tools(self) -> list:
"""Define computer use tools."""
return [
BetaToolComputerUse20241022(
type="computer_20241022",
name="computer",
display_width_px=self.screen_size[0],
display_height_px=self.screen_size[1],
),
BetaToolBash20241022(
type="bash_20241022",
name="bash",
),
BetaToolTextEditor20241022(
type="text_editor_20241022",
name="str_replace_editor",
),
]
def execute_tool(self, name: str, input: dict) -> dict:
"""Execute a tool and return result."""
if name == "computer":
return self._handle_computer_action(input)
elif name == "bash":
return self._handle_bash(input)
elif name == "str_replace_editor":
return self._handle_editor(input)
else:
return {"error": f"Unknown tool: {name}"}
def _handle_computer_action(self, input: dict) -> dict:
"""Handle computer control actions."""
action = input.get("action")
if action == "screenshot":
# Capture via xdotool/scrot
subprocess.run(["scrot", "/tmp/screenshot.png"])
with open("/tmp/screenshot.png", "rb") as f:
⚠️ Sharp Edges
| Issue | Severity | Solution |
|---|---|---|
| Issue | critical | ## Defense in depth - no single solution works |
| Issue | medium | ## Add human-like variance to actions |
| Issue | high | ## Use keyboard alternatives when possible |
| Issue | medium | ## Accept the tradeoff |
| Issue | high | ## Implement context management |
| Issue | high | ## Monitor and limit costs |
| Issue | critical | ## ALWAYS use sandboxing |
When to Use
This skill is applicable to execute the workflow or actions described in the overview.
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