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