| name | computer-use-agents |
| description | 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. |
| risk | unknown |
| source | vibeship-spawner-skills (Apache 2.0) |
| date_added | "2026-02-27T00:00:00.000Z" |
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:
- 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 {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
}}
]
messages.append({"role": "user", "content": user_content})
response = self.client.messages.create(
model=self.model,
max_tokens=1024,
system=system_prompt,
messages=messages
)
assistant_message = response.content[0].text
messages.append({"role": "assistant", "content": assistant_message})
# 3. Parse action from response
import json
try:
action = json.loads(assistant_message)
except json.JSONDecodeError:
# Try to extract JSON from response
import re
match = re.search(r'\{[^}]+\}', assistant_message)
if match:
action = json.loads(match.group())
else:
continue
# Check if done
if action.get("type") == "done":
return {
"success": True,
"result": action.get("result"),
"steps": step_count
}
# 4. ACTION: Execute
result = self.execute_action(action)
# Small delay for UI to update
time.sleep(self.action_delay)
return {
"success": False,
"error": "Max steps reached",
"steps": step_count
}
Usage
agent = ComputerUseAgent(Anthropic())
result = agent.run("Open Chrome and search for 'weather today'")
Anti_patterns
- Running without step limits (infinite loops)
- No delay between actions (UI can't keep up)
- Screenshots at full resolution (token explosion)
- Ignoring action failures (no recovery)
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 import dataclass
from typing import Optional
@dataclass
class SandboxConfig:
"""Configuration for agent sandbox."""
network_allowed: list[str] = None # Allowed domains
max_runtime_seconds: int = 300
max_memory_mb: int = 2048
allow_downloads: bool = False
allow_clipboard: bool = False
class SandboxedAgent:
"""
Run computer use agent in Docker sandbox.
"""
def __init__(self, config: SandboxConfig):
self.config = config
self.container_id: Optional[str] = None
def start(self):
"""Start sandboxed environment."""
# Build network rules
network_rules = ""
if self.config.network_allowed:
for domain in self.config.network_allowed:
network_rules += f"--add-host={domain}:$(dig +short {domain}) "
else:
network_rules = "--network=none"
cmd = f"""
docker run -d \
--name computer-use-sandbox-$$ \
--security-opt no-new-privileges \
--cap-drop ALL \
--memory {self.config.max_memory_mb}m \
--cpus 2 \
--read-only \
--tmpfs /tmp \
{network_rules} \
computer-use-agent:latest
"""
result = subprocess.run(cmd, shell=True, capture_output=True)
self.container_id = result.stdout.decode().strip()
# Set up kill timer
subprocess.Popen([
"sh", "-c",
f"sleep {self.config.max_runtime_seconds} && docker kill {self.container_id}"
])
return self.container_id
def execute_task(self, task: str) -> dict:
"""Execute task in sandbox."""
if not self.container_id:
self.start()
# Send task to agent via API
import requests
response = requests.post(
f"http://localhost:8080/task",
json={"task": task},
timeout=self.config.max_runtime_seconds
)
return response.json()
def stop(self):
"""Stop and remove sandbox."""
if self.container_id:
subprocess.run(f"docker rm -f {self.container_id}", shell=True)
self.container_id = None
Anti_patterns
- Running agents on host system directly
- Giving sandbox full network access
- Running as root in container
- No resource limits (denial of service)
- Persistent storage (data can leak between runs)
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:
img_data = f.read()
# Resize for efficiency
img = Image.open(io.BytesIO(img_data))
img = img.resize(self.screen_size, Image.LANCZOS)
buffer = io.BytesIO()
img.save(buffer, format="PNG")
return {
"type": "image",
"source": {
"type": "base64",
"media_type": "image/png",
"data": base64.b64encode(buffer.getvalue()).decode()
}
}
elif action == "mouse_move":
x, y = input.get("coordinate", [0, 0])
subprocess.run(["xdotool", "mousemove", str(x), str(y)])
return {"success": True}
elif action == "left_click":
subprocess.run(["xdotool", "click", "1"])
return {"success": True}
elif action == "right_click":
subprocess.run(["xdotool", "click", "3"])
return {"success": True}
elif action == "double_click":
subprocess.run(["xdotool", "click", "--repeat", "2", "1"])
return {"success": True}
elif action == "type":
text = input.get("text", "")
# Use xdotool type with delay for reliability
subprocess.run(["xdotool", "type", "--delay", "50", text])
return {"success": True}
elif action == "key":
key = input.get("key", "")
# Map common key names
key_map = {
"return": "Return",
"enter": "Return",
"tab": "Tab",
"escape": "Escape",
"backspace": "BackSpace",
}
xdotool_key = key_map.get(key.lower(), key)
subprocess.run(["xdotool", "key", xdotool_key])
return {"success": True}
elif action == "scroll":
direction = input.get("direction", "down")
amount = input.get("amount", 3)
button = "5" if direction == "down" else "4"
for _ in range(amount):
subprocess.run(["xdotool", "click", button])
return {"success": True}
return {"error": f"Unknown action: {action}"}
def _handle_bash(self, input: dict) -> dict:
"""Execute bash command."""
command = input.get("command", "")
# Security: Sanitize and limit commands
dangerous_patterns = ["rm -rf", "mkfs", "dd if=", "> /dev/"]
for pattern in dangerous_patterns:
if pattern in command:
return {"error": "Dangerous command blocked"}
try:
result = subprocess.run(
command,
shell=True,
capture_output=True,
text=True,
timeout=30
)
return {
"stdout": result.stdout[:10000], # Limit output
"stderr": result.stderr[:1000],
"returncode": result.returncode
}
except subprocess.TimeoutExpired:
return {"error": "Command timed out"}
def _handle_editor(self, input: dict) -> dict:
"""Handle text editor operations."""
command = input.get("command")
path = input.get("path")
if command == "view":
try:
with open(path, "r") as f:
content = f.read()
return {"content": content[:50000]} # Limit size
except Exception as e:
return {"error": str(e)}
elif command == "str_replace":
old_str = input.get("old_str")
new_str = input.get("new_str")
try:
with open(path, "r") as f:
content = f.read()
if old_str not in content:
return {"error": "old_str not found in file"}
content = content.replace(old_str, new_str, 1)
with open(path, "w") as f:
f.write(content)
return {"success": True}
except Exception as e:
return {"error": str(e)}
return {"error": f"Unknown editor command: {command}"}
def run_task(self, task: str, max_steps: int = 50) -> dict:
"""Run computer use task with agentic loop."""
messages = [{"role": "user", "content": task}]
tools = self.get_tools()
for step in range(max_steps):
response = self.client.beta.messages.create(
model=self.model,
max_tokens=4096,
tools=tools,
messages=messages,
betas=["computer-use-2024-10-22"]
)
# Check for completion
if response.stop_reason == "end_turn":
return {
"success": True,
"result": response.content[0].text if response.content else "",
"steps": step + 1
}
# Handle tool use
if response.stop_reason == "tool_use":
messages.append({"role": "assistant", "content": response.content})
tool_results = []
for block in response.content:
if block.type == "tool_use":
result = self.execute_tool(block.name, block.input)
tool_results.append({
"type": "tool_result",
"tool_use_id": block.id,
"content": result
})
messages.append({"role": "user", "content": tool_results})
return {"success": False, "error": "Max steps reached"}
Anti_patterns
- Not using betas=['computer-use-2024-10-22'] flag
- Full resolution screenshots (wasteful)
- No command sanitization for bash tool
- Unbounded execution time
Browser-Use Pattern (Playwright-based)
For browser-only automation, using structured DOM access is more efficient
than pixel-based computer use. Playwright MCP allows LLMs to control
browsers using accessibility snapshots rather than screenshots.
Advantages over vision-based:
- Faster: No image processing required
- Cheaper: Text tokens vs image tokens
- More precise: Direct element targeting
- More reliable: No coordinate drift
When to use vision vs structured:
- Vision: Desktop apps, complex UIs, visual verification
- Structured: Web automation, form filling, data extraction
When to use: Browser-only automation tasks,Form filling and web interactions,When speed and cost matter more than visual understanding
from playwright.async_api import async_playwright
from dataclasses import dataclass
from typing import Optional
import asyncio
@dataclass
class BrowserAction:
"""Structured browser action."""
action: str # click, type, navigate, scroll, extract
selector: Optional[str] = None
text: Optional[str] = None
url: Optional[str] = None
class BrowserUseAgent:
"""
Browser automation using Playwright with structured commands.
More efficient than pixel-based for web tasks.
"""
def __init__(self):
self.browser = None
self.page = None
async def start(self, headless: bool = True):
"""Start browser session."""
self.playwright = await async_playwright().start()
self.browser = await self.playwright.chromium.launch(headless=headless)
self.page = await self.browser.new_page()
async def get_page_snapshot(self) -> dict:
"""
Get structured snapshot of page for LLM.
Uses accessibility tree for efficiency.
"""
# Get accessibility tree
snapshot = await self.page.accessibility.snapshot()
# Get simplified DOM info
elements = await self.page.evaluate('''() => {
const interactable = [];
const selector = 'a, button, input, select, textarea, [role="button"]';
document.querySelectorAll(selector).forEach((el, i) => {
const rect = el.getBoundingClientRect();
if (rect.width > 0 && rect.height > 0) {
interactable.push({
index: i,
tag: el.tagName.toLowerCase(),
text: el.textContent?.trim().slice(0, 100),
type: el.type,
placeholder: el.placeholder,
name: el.name,
id: el.id,
class: el.className
});
}
});
return interactable;
}''')
return {
"url": self.page.url,
"title": await self.page.title(),
"accessibility_tree": snapshot,
"interactable_elements": elements[:50] # Limit for token efficiency
}
async def execute_action(self, action: BrowserAction) -> dict:
"""Execute structured browser action."""
try:
if action.action == "navigate":
await self.page.goto(action.url, wait_until="domcontentloaded")
return {"success": True, "url": self.page.url}
elif action.action == "click":
await self.page.click(action.selector, timeout=5000)
await self.page.wait_for_load_state("networkidle", timeout=5000)
return {"success": True}
elif action.action == "type":
await self.page.fill(action.selector, action.text)
return {"success": True}
elif action.action == "scroll":
direction = action.text or "down"
distance = 500 if direction == "down" else -500
await self.page.evaluate(f"window.scrollBy(0, {distance})")
return {"success": True}
elif action.action == "extract":
# Extract text content
if action.selector:
text = await self.page.text_content(action.selector)
else:
text = await self.page.text_content("body")
return {"success": True, "text": text[:5000]}
elif action.action == "screenshot":
# Fall back to vision when needed
screenshot = await self.page.screenshot(type="png")
import base64
return {
"success": True,
"image": base64.b64encode(screenshot).decode()
}
except Exception as e:
return {"success": False, "error": str(e)}
return {"success": False, "error": f"Unknown action: {action.action}"}
async def run_with_llm(self, task: str, llm_client, max_steps: int = 20):
"""
Run browser task with LLM decision making.
Uses structured DOM instead of screenshots.
"""
system_prompt = """You are a browser automation agent. You receive
page snapshots with interactable elements and decide actions.
Respond with JSON action:
- {"action": "navigate", "url": "https://..."}
- {"action": "click", "selector": "button.submit"}
- {"action": "type", "selector": "input[name='email']", "text": "..."}
- {"action": "scroll", "text": "down"}
- {"action": "extract", "selector": ".results"}
- {"action": "done", "result": "task completed"}
Use CSS selectors based on the element info provided.
Prefer id > name > class > text content for selectors.
"""
messages = []
for step in range(max_steps):
# Get current page state
snapshot = await self.get_page_snapshot()
user_message = f"""Task: {task}
Current page:
URL: {snapshot['url']}
Title: {snapshot['title']}
Interactable elements:
{snapshot['interactable_elements']}
What action should I take?"""
messages.append({"role": "user", "content": user_message})
# Get LLM decision
response = llm_client.messages.create(
model="claude-sonnet-4-20250514",
max_tokens=1024,
system=system_prompt,
messages=messages
)
assistant_text = response.content[0].text
messages.append({"role": "assistant", "content": assistant_text})
# Parse and execute
import json
action_dict = json.loads(assistant_text)
if action_dict.get("action") == "done":
return {"success": True, "result": action_dict.get("result")}
action = BrowserAction(**action_dict)
result = await self.execute_action(action)
if not result.get("success"):
messages.append({
"role": "user",
"content": f"Action failed: {result.get('error')}"
})
await asyncio.sleep(0.5) # Rate limit
return {"success": False, "error": "Max steps reached"}
async def close(self):
"""Clean up browser."""
if self.browser:
await self.browser.close()
if hasattr(self, 'playwright'):
await self.playwright.stop()
Usage
async def main():
agent = BrowserUseAgent()
await agent.start(headless=False)
from anthropic import Anthropic
result = await agent.run_with_llm(
"Go to weather.com and find the weather for New York",
Anthropic()
)
print(result)
await agent.close()
asyncio.run(main())
Anti_patterns
- Using screenshots when DOM access works
- Not waiting for page loads
- Hardcoded selectors that break
- No error recovery for stale elements
User Confirmation Pattern
For sensitive actions, agents should pause and ask for human confirmation.
"ChatGPT agent also pauses and asks for confirmation prior to taking
sensitive steps such as completing a purchase."
Sensitivity levels:
- LOW: Navigation, reading (auto-approve)
- MEDIUM: Form filling, clicking (log, maybe confirm)
- HIGH: Purchases, authentication, file operations (always confirm)
- CRITICAL: Credential entry, financial transactions (confirm + review)
When to use: Actions with real-world consequences,Financial transactions,Authentication flows,File modifications
from enum import Enum
from dataclasses import dataclass
from typing import Callable, Optional
import asyncio
class ActionSeverity(Enum):
LOW = "low" # Auto-approve
MEDIUM = "medium" # Log, optional confirm
HIGH = "high" # Always confirm
CRITICAL = "critical" # Confirm + review details
@dataclass
class SensitiveAction:
"""Action that may need user confirmation."""
action_type: str
description: str
severity: ActionSeverity
details: dict
class ConfirmationGate:
"""
Gate sensitive actions through user confirmation.
"""
# Action type -> severity mapping
ACTION_SEVERITY = {
# LOW - auto-approve
"navigate": ActionSeverity.LOW,
"scroll": ActionSeverity.LOW,
"read": ActionSeverity.LOW,
"screenshot": ActionSeverity.LOW,
# MEDIUM - log and maybe confirm
"click": ActionSeverity.MEDIUM,
"type": ActionSeverity.MEDIUM,
"search": ActionSeverity.MEDIUM,
# HIGH - always confirm
"download": ActionSeverity.HIGH,
"submit_form": ActionSeverity.HIGH,
"login": ActionSeverity.HIGH,
"file_write": ActionSeverity.HIGH,
# CRITICAL - confirm with full review
"purchase": ActionSeverity.CRITICAL,
"enter_password": ActionSeverity.CRITICAL,
"enter_credit_card": ActionSeverity.CRITICAL,
"send_money": ActionSeverity.CRITICAL,
"delete": ActionSeverity.CRITICAL,
}
def __init__(
self,
confirm_callback: Callable[[SensitiveAction], bool] = None,
auto_confirm_low: bool = True,
auto_confirm_medium: bool = False
):
self.confirm_callback = confirm_callback or self._default_confirm
self.auto_confirm_low = auto_confirm_low
self.auto_confirm_medium = auto_confirm_medium
self.action_log = []
def _default_confirm(self, action: SensitiveAction) -> bool:
"""Default confirmation via CLI prompt."""
print(f"\n{'='*60}")
print(f"ACTION CONFIRMATION REQUIRED")
print(f"{'='*60}")
print(f"Type: {action.action_type}")
print(f"Severity: {action.severity.value.upper()}")
print(f"Description: {action.description}")
print(f"Details: {action.details}")
print(f"{'='*60}")
while True:
response = input("Allow this action? [y/n]: ").lower().strip()
if response in ['y', 'yes']:
return True
elif response in ['n', 'no']:
return False
def classify_action(self, action_type: str, context: dict) -> ActionSeverity:
"""Classify action severity, considering context."""
base_severity = self.ACTION_SEVERITY.get(action_type, ActionSeverity.MEDIUM)
# Escalate based on context
if context.get("involves_credentials"):
return ActionSeverity.CRITICAL
if context.get("involves_money"):
return ActionSeverity.CRITICAL
if context.get("irreversible"):
return max(base_severity, ActionSeverity.HIGH, key=lambda x: x.value)
return base_severity
def check_action(
self,
action_type: str,
description: str,
details: dict = None
) -> tuple[bool, str]:
"""
Check if action should proceed.
Returns (approved, reason).
"""
details = details or {}
severity = self.classify_action(action_type, details)
action = SensitiveAction(
action_type=action_type,
description=description,
severity=severity,
details=details
)
# Log all actions
self.action_log.append({
"action": action,
"timestamp": __import__('datetime').datetime.now().isoformat()
})
# Auto-approve low severity
if severity == ActionSeverity.LOW and self.auto_confirm_low:
return True, "auto-approved (low severity)"
# Maybe auto-approve medium
if severity == ActionSeverity.MEDIUM and self.auto_confirm_medium:
return True, "auto-approved (medium severity)"
# Request confirmation
approved = self.confirm_callback(action)
if approved:
return True, "user approved"
else:
return False, "user rejected"
class ConfirmedComputerUseAgent:
"""
Computer use agent with confirmation gates.
"""
def __init__(self, base_agent, confirmation_gate: ConfirmationGate):
self.agent = base_agent
self.gate = confirmation_gate
def execute_action(self, action: dict) -> dict:
"""Execute action with confirmation check."""
action_type = action.get("type", "unknown")
# Build description
if action_type == "click":
desc = f"Click at ({action.get('x')}, {action.get('y')})"
elif action_type == "type":
text = action.get('text', '')
# Mask if looks like password
if self._looks_sensitive(text):
desc = f"Type sensitive text ({len(text)} chars)"
else:
desc = f"Type: {text[:50]}..."
else:
desc = f"Execute: {action_type}"
# Context for severity classification
context = {
"involves_credentials": self._looks_sensitive(action.get("text", "")),
"involves_money": self._mentions_money(action),
}
# Check with gate
approved, reason = self.gate.check_action(
action_type, desc, context
)
if not approved:
return {
"success": False,
"error": f"Action blocked: {reason}",
"action": action_type
}
# Execute if approved
return self.agent.execute_action(action)
def _looks_sensitive(self, text: str) -> bool:
"""Check if text looks like sensitive data."""
if not text:
return False
# Common patterns
patterns = [
r'\b\d{16}\b', # Credit card
r'\b\d{3,4}\b.*\b\d{3,4}\b', # CVV-like
r'password',
r'secret',
r'api.?key',
r'token'
]
import re
return any(re.search(p, text.lower()) for p in patterns)
def _mentions_money(self, action: dict) -> bool:
"""Check if action involves money."""
text = str(action)
money_patterns = [
r'\$\d+', r'pay', r'purchase', r'buy', r'checkout',
r'credit', r'debit', r'invoice', r'payment'
]
import re
return any(re.search(p, text.lower()) for p in money_patterns)
Usage
gate = ConfirmationGate(
auto_confirm_low=True,
auto_confirm_medium=False # Confirm clicks, typing
)
agent = ConfirmedComputerUseAgent(base_agent, gate)
result = agent.execute_action({"type": "click", "x": 500, "y": 300})
Anti_patterns
- Auto-approving all actions
- Not logging rejected actions
- Showing full passwords in confirmation
- No timeout on confirmation (hangs forever)
Action Logging Pattern
All computer use agent actions should be logged for:
- Debugging failed automations
- Security auditing
- Reproducibility
- Compliance requirements
Log format should capture:
- Timestamp
- Action type and parameters
- Screenshot before/after
- Success/failure status
- Model reasoning (if available)
When to use: Production computer use deployments,Debugging automation failures,Security-sensitive environments
from dataclasses import dataclass, field
from datetime import datetime
from typing import Optional, Any
import json
import os
@dataclass
class ActionLogEntry:
"""Single action log entry."""
timestamp: datetime
action_type: str
parameters: dict
success: bool
error: Optional[str] = None
screenshot_before: Optional[str] = None # Path to screenshot
screenshot_after: Optional[str] = None
model_reasoning: Optional[str] = None
duration_ms: Optional[int] = None
def to_dict(self) -> dict:
return {
"timestamp": self.timestamp.isoformat(),
"action_type": self.action_type,
"parameters": self._sanitize_params(self.parameters),
"success": self.success,
"error": self.error,
"screenshot_before": self.screenshot_before,
"screenshot_after": self.screenshot_after,
"model_reasoning": self.model_reasoning,
"duration_ms": self.duration_ms
}
def _sanitize_params(self, params: dict) -> dict:
"""Remove sensitive data from params."""
sanitized = {}
sensitive_keys = ['password', 'secret', 'token', 'key', 'credit_card']
for k, v in params.items():
if any(s in k.lower() for s in sensitive_keys):
sanitized[k] = "[REDACTED]"
elif isinstance(v, str) and len(v) > 100:
sanitized[k] = v[:100] + "...[truncated]"
else:
sanitized[k] = v
return sanitized
@dataclass
class TaskSession:
"""A complete task execution session."""
session_id: str
task: str
start_time: datetime
end_time: Optional[datetime] = None
actions: list[ActionLogEntry] = field(default_factory=list)
success: bool = False
final_result: Optional[str] = None
class ActionLogger:
"""
Comprehensive action logging for computer use agents.
"""
def __init__(self, log_dir: str = "./agent_logs"):
self.log_dir = log_dir
self.screenshot_dir = os.path.join(log_dir, "screenshots")
os.makedirs(self.screenshot_dir, exist_ok=True)
self.current_session: Optional[TaskSession] = None
def start_session(self, task: str) -> str:
"""Start a new task session."""
import uuid
session_id = str(uuid.uuid4())[:8]
self.current_session = TaskSession(
session_id=session_id,
task=task,
start_time=datetime.now()
)
return session_id
def log_action(
self,
action_type: str,
parameters: dict,
success: bool,
error: Optional[str] = None,
screenshot_before: bytes = None,
screenshot_after: bytes = None,
model_reasoning: str = None,
duration_ms: int = None
):
"""Log a single action."""
if not self.current_session:
raise RuntimeError("No active session")
# Save screenshots if provided
screenshot_paths = {}
timestamp_str = datetime.now().strftime("%Y%m%d_%H%M%S_%f")
if screenshot_before:
path = os.path.join(
self.screenshot_dir,
f"{self.current_session.session_id}_{timestamp_str}_before.png"
)
with open(path, "wb") as f:
f.write(screenshot_before)
screenshot_paths["before"] = path
if screenshot_after:
path = os.path.join(
self.screenshot_dir,
f"{self.current_session.session_id}_{timestamp_str}_after.png"
)
with open(path, "wb") as f:
f.write(screenshot_after)
screenshot_paths["after"] = path
# Create log entry
entry = ActionLogEntry(
timestamp=datetime.now(),
action_type=action_type,
parameters=parameters,
success=success,
error=error,
screenshot_before=screenshot_paths.get("before"),
screenshot_after=screenshot_paths.get("after"),
model_reasoning=model_reasoning,
duration_ms=duration_ms
)
self.current_session.actions.append(entry)
# Also append to running log file
self._append_to_log(entry)
def _append_to_log(self, entry: ActionLogEntry):
"""Append entry to JSONL log file."""
log_file = os.path.join(
self.log_dir,
f"session_{self.current_session.session_id}.jsonl"
)
with open(log_file, "a") as f:
f.write(json.dumps(entry.to_dict()) + "\n")
def end_session(self, success: bool, result: str = None):
"""End current session."""
if not self.current_session:
return
self.current_session.end_time = datetime.now()
self.current_session.success = success
self.current_session.final_result = result
# Write session summary
summary_file = os.path.join(
self.log_dir,
f"session_{self.current_session.session_id}_summary.json"
)
summary = {
"session_id": self.current_session.session_id,
"task": self.current_session.task,
"start_time": self.current_session.start_time.isoformat(),
"end_time": self.current_session.end_time.isoformat(),
"duration_seconds": (
self.current_session.end_time -
self.current_session.start_time
).total_seconds(),
"total_actions": len(self.current_session.actions),
"successful_actions": sum(
1 for a in self.current_session.actions if a.success
),
"failed_actions": sum(
1 for a in self.current_session.actions if not a.success
),
"success": success,
"final_result": result
}
with open(summary_file, "w") as f:
json.dump(summary, f, indent=2)
self.current_session = None
def get_session_replay(self, session_id: str) -> list[dict]:
"""Get all actions from a session for replay/debugging."""
log_file = os.path.join(self.log_dir, f"session_{session_id}.jsonl")
actions = []
with open(log_file, "r") as f:
for line in f:
actions.append(json.loads(line))
return actions
Integration with agent
class LoggedComputerUseAgent:
"""Computer use agent with comprehensive logging."""
def __init__(self, base_agent, logger: ActionLogger):
self.agent = base_agent
self.logger = logger
def run_task(self, task: str) -> dict:
"""Run task with full logging."""
session_id = self.logger.start_session(task)
try:
result = self._run_with_logging(task)
self.logger.end_session(
success=result.get("success", False),
result=result.get("result")
)
return result
except Exception as e:
self.logger.end_session(success=False, result=str(e))
raise
def _run_with_logging(self, task: str) -> dict:
"""Internal run with action logging."""
# This would wrap the base agent's run method
# and log each action
pass
Anti_patterns
- Not sanitizing sensitive data in logs
- Storing screenshots indefinitely (storage costs)
- Not rotating log files
- Logging synchronously (blocks agent)
Sharp Edges
Web Content Can Hijack Your Agent
Severity: CRITICAL
Situation: Computer use agent browsing the web
Symptoms:
Agent suddenly performs unexpected actions. Clicks malicious links.
Enters credentials on phishing sites. Downloads files it shouldn't.
Ignores your instructions and follows embedded commands instead.
Why this breaks:
"While all agents that process untrusted content are subject to prompt
injection risks, browser use amplifies this risk in two ways. First,
the attack surface is vast: every webpage, embedded document, advertisement,
and dynamically loaded script represents a potential vector for malicious
instructions. Second, browser agents can take many different actions—
navigating to URLs, filling forms, clicking buttons, downloading files—
that attackers can exploit."
Real attacks have already happened:
- "Microsoft Copilot agents were hijacked with emails containing malicious
instructions, which allowed attackers to extract entire CRM databases."
- "Google's Workspace services were manipulated—hidden prompts inside
calendar invites and emails tricked Gemini agents into deleting events
and exposing sensitive messages."
Even a 1% attack success rate represents meaningful risk at scale.
Recommended fix:
Defense in depth - no single solution works
-
Sandboxing (most effective):
docker run \
--security-opt no-new-privileges \
--cap-drop ALL \
--network none \
--read-only \
computer-use-agent
-
Classifier-based detection:
def scan_for_injection(content: str) -> bool:
"""Detect prompt injection attempts."""
patterns = [
r"ignore.*instructions",
r"disregard.*previous",
r"new.*instructions",
r"you are now",
r"act as if",
r"pretend to be",
]
return any(re.search(p, content.lower()) for p in patterns)
page_text = await page.text_content("body")
if scan_for_injection(page_text):
return {"error": "Potential injection detected"}
-
User confirmation for sensitive actions:
SENSITIVE_ACTIONS = {"download", "submit", "login", "purchase"}
if action_type in SENSITIVE_ACTIONS:
if not await get_user_confirmation(action):
return {"error": "User rejected action"}
-
Scoped credentials:
- Never give agent access to all credentials
- Use temporary, limited tokens
- Revoke after task completion
Vision Agents Click Exact Centers
Severity: MEDIUM
Situation: Agent clicking on UI elements
Symptoms:
Agent's clicks are detectable as non-human. Websites may block or
CAPTCHA the agent. Anti-bot systems flag the interaction.
Why this breaks:
"When a vision model identifies a button, it calculates the center.
Click coordinates land at mathematically precise positions—often exact
element centers or grid-aligned pixel values. Humans don't click centers;
their click distributions follow a Gaussian pattern around targets."
The screenshot loop also creates detectable patterns:
"Predictable pauses. Vision agents are completely still during their
'thinking' phase. The pattern looks like: Action → Complete stillness
(1-5 seconds) → Action → Complete stillness → Action."
Sophisticated anti-bot systems detect:
- Perfect center clicks
- No mouse movement during "thinking"
- Consistent timing between actions
- Lack of micro-movements and hesitation
Recommended fix:
Add human-like variance to actions
import random
import time
def humanized_click(x: int, y: int) -> tuple[int, int]:
"""Add human-like variance to click coordinates."""
x_offset = int(random.gauss(0, 5))
y_offset = int(random.gauss(0, 5))
return (x + x_offset, y + y_offset)
def humanized_delay():
"""Add human-like delay between actions."""
base_delay = random.uniform(0.3, 0.8)
if random.random() < 0.2:
base_delay += random.uniform(0.5, 2.0)
time.sleep(base_delay)
def humanized_movement(from_pos: tuple, to_pos: tuple):
"""Move mouse in curved path like human."""
steps = random.randint(10, 20)
for i in range(steps):
t = i / steps
x = from_pos[0] + (to_pos[0] - from_pos[0]) * t
y = from_pos[1] + (to_pos[1] - from_pos[1]) * t
x += random.gauss(0, 2)
y += random.gauss(0, 2)
pyautogui.moveTo(int(x), int(y))
time.sleep(0.01)
Rotate user agents and fingerprints
USER_AGENTS = [
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) Chrome/120...",
"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) Safari/...",
]
await page.set_extra_http_headers({
"User-Agent": random.choice(USER_AGENTS)
})
Dropdowns, Scrollbars, and Drags Are Unreliable
Severity: HIGH
Situation: Agent interacting with complex UI elements
Symptoms:
Agent fails to select dropdown options. Scroll doesn't work as expected.
Drag and drop completely fails. Hover menus disappear before clicking.
Why this breaks:
"Computer Use currently struggles with certain interface interactions,
particularly scrolling, dragging, and zooming operations. Some UI elements
(like dropdowns and scrollbars) might be tricky for Claude to manipulate."
Why these are hard:
- Dropdowns: Options appear after click, need second click to select
- Scrollbars: Small targets, need precise positioning
- Drag: Requires coordinated mouse down, move, mouse up
- Hover menus: Disappear when mouse moves away
- Canvas elements: No semantic information visible
Vision models see pixels, not DOM structure. They don't "know" that
a dropdown is a dropdown - they have to infer from visual cues.
Recommended fix:
Use keyboard alternatives when possible
async def select_dropdown_option(page, dropdown_selector, option_text):
await page.click(dropdown_selector)
await asyncio.sleep(0.3)
await page.keyboard.type(option_text[:3])
await asyncio.sleep(0.2)
await page.keyboard.press("Enter")
Break complex actions into steps
async def reliable_drag(page, source, target):
await page.mouse.move(source["x"], source["y"])
await page.mouse.down()
await asyncio.sleep(0.2)
steps = 10
for i in range(steps):
x = source["x"] + (target["x"] - source["x"]) * i / steps
y = source["y"] + (target["y"] - source["y"]) * i / steps
await page.mouse.move(x, y)
await asyncio.sleep(0.05)
await page.mouse.move(target["x"], target["y"])
await asyncio.sleep(0.1)
await page.mouse.up()
Fall back to DOM access for web
async def robust_select(page, select_selector, value):
try:
await vision_agent.select(select_selector, value)
except Exception:
await page.select_option(select_selector, value=value)
Add verification after action
async def verified_scroll(page, direction):
before = await page.evaluate("window.scrollY")
await page.mouse.wheel(0, 500 if direction == "down" else -500)
await asyncio.sleep(0.3)
after = await page.evaluate("window.scrollY")
if before == after:
await page.keyboard.press("PageDown" if direction == "down" else "PageUp")
Agents Are 2-5x Slower Than Humans
Severity: MEDIUM
Situation: Automating any computer task
Symptoms:
Task that takes human 1 minute takes agent 3-5 minutes.
Users complain about speed. Timeouts occur.
Why this breaks:
"The technology can be slow compared to human operators, often requiring
multiple screenshots and analysis cycles."
Why so slow:
- Screenshot capture: 100-500ms
- Vision model inference: 1-5 seconds per screenshot
- Action execution: 200-500ms
- Wait for UI update: 500-1000ms
- Total per action: 2-7 seconds
A task requiring 20 actions takes 40-140 seconds minimum.
Humans do the same actions in 20-30 seconds.
Recommended fix:
Accept the tradeoff
Computer use is for:
- Tasks humans don't want to do (repetitive)
- Tasks that can run in background
- Tasks where accuracy > speed
Optimize where possible
SCREEN_SIZE = (1280, 800)
await page.type("hello world")
simple_model = "claude-haiku-..."
complex_model = "claude-sonnet-..."
Set realistic expectations
def estimate_duration(task_complexity: str) -> int:
"""Estimate task duration in seconds."""
estimates = {
"simple": 30,
"medium": 120,
"complex": 300,
}
return estimates.get(task_complexity, 120)
estimated = estimate_duration("medium")
print(f"Estimated completion: {estimated // 60}m {estimated % 60}s")
Screenshots Fill Up Context Window Fast
Severity: HIGH
Situation: Long-running computer use tasks
Symptoms:
Agent forgets earlier steps. Starts repeating actions.
Errors increase as task progresses. Costs explode.
Why this breaks:
Each screenshot is ~1500-3000 tokens. A task with 30 screenshots
uses 45,000-90,000 tokens just for images - before any text.
Claude's context window is finite. When full:
- Older context gets dropped
- Agent loses memory of earlier steps
- Task coherence decreases
"Getting agents to make consistent progress across multiple context
windows remains an open problem. The core challenge is that they must
work in discrete sessions, and each new session begins with no memory
of what came before." - Anthropic engineering blog
Recommended fix:
Implement context management
class ContextManager:
"""Manage context window usage for computer use."""
MAX_SCREENSHOTS = 10
MAX_TOKENS = 100000
def __init__(self):
self.messages = []
self.screenshot_count = 0
def add_screenshot(self, screenshot_b64: str, description: str):
"""Add screenshot with automatic pruning."""
self.screenshot_count += 1
if self.screenshot_count > self.MAX_SCREENSHOTS:
self._prune_old_screenshots()
self.messages.append({
"role": "user",
"content": [
{"type": "text", "text": description},
{"type": "image", "source": {...}}
]
})
def _prune_old_screenshots(self):
"""Remove old screenshots, keep text summaries."""
new_messages = []
screenshots_kept = 0
for msg in reversed(self.messages):
if self._has_image(msg):
if screenshots_kept < self.MAX_SCREENSHOTS:
new_messages.insert(0, msg)
screenshots_kept += 1
else:
summary = self._summarize_screenshot(msg)
new_messages.insert(0, {
"role": msg["role"],
"content": summary
})
else:
new_messages.insert(0, msg)
self.messages = new_messages
def _summarize_screenshot(self, msg) -> str:
"""Summarize screenshot to text."""
for content in msg.get("content", []):
if content.get("type") == "text":
return f"[Previous screenshot: {content['text']}]"
return "[Previous screenshot - details pruned]"
def add_checkpoint(self):
"""Create a checkpoint summary."""
summary = self._create_progress_summary()
self.messages.append({
"role": "user",
"content": f"CHECKPOINT: {summary}"
})
Use checkpointing for long tasks
async def run_with_checkpoints(task: str, checkpoint_every: int = 10):
"""Run task with periodic checkpoints."""
context = ContextManager()
step = 0
while not task_complete:
step += 1
if step % checkpoint_every == 0:
context.add_checkpoint()
save_checkpoint(context, step)
Break into subtasks
subtasks = [
"Navigate to the website and login",
"Find the settings page",
"Update the email address to ...",
"Save and verify the change"
]
for subtask in subtasks:
result = await agent.run(subtask)
if not result["success"]:
handle_error(subtask, result)
break
Costs Can Explode Quickly
Severity: HIGH
Situation: Running computer use at scale
Symptoms:
API bill is 10x higher than expected. Single task costs $5+ instead of $0.50.
Monthly costs reach thousands of dollars quickly.
Why this breaks:
Vision tokens are expensive. Each screenshot:
- ~2000-3000 tokens per image
- At $10/million tokens, that's $0.02-0.03 per screenshot
- Task with 30 screenshots = $0.60-0.90 just for images
But it compounds:
- Screenshots accumulate in context
- Model sees ALL previous screenshots each turn
- Turn 10 processes 10 screenshots = $0.20-0.30
- Turn 20 processes 20 screenshots = $0.40-0.60
- Quadratic growth!
Complex task: 50 turns × average 25 images in context = 1250 image tokens
Plus text = could easily hit $5-10 per task.
Recommended fix:
Monitor and limit costs
class CostTracker:
"""Track and limit computer use costs."""
INPUT_COST_PER_1K = 0.003
OUTPUT_COST_PER_1K = 0.015
IMAGE_COST_PER_1K = 0.01
def __init__(self, max_cost_per_task: float = 1.0):
self.max_cost = max_cost_per_task
self.current_cost = 0.0
self.total_tokens = 0
def add_turn(
self,
input_tokens: int,
output_tokens: int,
image_tokens: int
):
"""Track cost of a single turn."""
cost = (
input_tokens / 1000 * self.INPUT_COST_PER_1K +
output_tokens / 1000 * self.OUTPUT_COST_PER_1K +
image_tokens / 1000 * self.IMAGE_COST_PER_1K
)
self.current_cost += cost
self.total_tokens += input_tokens + output_tokens + image_tokens
if self.current_cost > self.max_cost:
raise CostLimitExceeded(
f"Cost limit exceeded: ${self.current_cost:.2f} > ${self.max_cost:.2f}"
)
return cost
class CostLimitExceeded(Exception):
pass
tracker = CostTracker(max_cost_per_task=2.0)
try:
for turn in turns:
tracker.add_turn(turn.input, turn.output, turn.images)
except CostLimitExceeded:
print("Task aborted due to cost limit")
Reduce image costs
SCREEN_SIZE = (1024, 768)
screenshot.save(buffer, format="JPEG", quality=70)
def crop_relevant(screenshot: Image, focus_area: tuple):
"""Crop to area of interest."""
return screenshot.crop(focus_area)
if not needs_visual_update:
messages.append({"role": "user", "content": "Continue..."})
Use cheaper models strategically
async def tiered_model_selection(task_complexity: str):
"""Use appropriate model for task."""
if task_complexity == "simple":
return "claude-haiku-..."
elif task_complexity == "medium":
return "claude-sonnet-4-20250514"
else:
return "claude-opus-4-5-..."
Running Agent on Your Actual Computer
Severity: CRITICAL
Situation: Testing or deploying computer use
Symptoms:
Agent deletes important files. Sends emails from your account.
Posts on social media. Accesses sensitive documents.
Why this breaks:
Computer use agents make mistakes. They can:
- Misinterpret instructions
- Click wrong buttons
- Type in wrong fields
- Follow prompt injection attacks
Without sandboxing, these mistakes happen on your real system.
There's no undo for "agent sent email to all contacts" or
"agent deleted project folder."
"Autonomous agents that can access external systems and APIs
introduce new security risks. They may be vulnerable to prompt
injection attacks, unauthorized access to sensitive data, or
manipulation by malicious actors."
Recommended fix:
ALWAYS use sandboxing
docker run -it --rm \
--security-opt no-new-privileges \
--cap-drop ALL \
--network none \
--read-only \
--tmpfs /tmp \
--memory 2g \
--cpus 1 \
computer-use-sandbox
Layer your defenses
@dataclass
class SandboxConfig:
docker_image: str = "computer-use-sandbox:latest"
network: str = "none"
readonly_root: bool = True
max_memory_mb: int = 2048
max_cpu: float = 1.0
max_runtime_seconds: int = 300
require_confirmation: list = field(default_factory=lambda: [
"download", "submit", "login", "delete"
])
log_all_actions: bool = True
Test in isolated environment first
class SandboxedTestRunner:
"""Run tests in throwaway containers."""
async def run_test(self, test_task: str) -> dict:
container_id = await self.create_container()
try:
result = await self.execute_in_container(container_id, test_task)
state = await self.capture_container_state(container_id)
return {
"result": result,
"final_state": state,
"logs": await self.get_logs(container_id)
}
finally:
await self.destroy_container(container_id)
Validation Checks
Computer Use Without Sandbox
Severity: ERROR
Computer use agents MUST run in sandboxed environments
Message: Computer use without sandboxing detected. Use Docker containers with restrictions.
Sandbox With Full Network Access
Severity: ERROR
Sandboxed agents should have restricted network access
Message: Sandbox has full network access. Use --network=none or specific allowlist.
Running as Root in Container
Severity: ERROR
Container agents should run as non-root user
Message: Container running as root. Add --user flag or USER directive in Dockerfile.
Container Without Capability Drops
Severity: WARNING
Containers should drop unnecessary capabilities
Message: Container has full capabilities. Add --cap-drop ALL.
Container Without Seccomp Profile
Severity: WARNING
Containers should use seccomp profiles for syscall filtering
Message: No security options set. Consider --security-opt seccomp:profile.json
No Maximum Step Limit
Severity: WARNING
Computer use loops should have maximum step limits
Message: Infinite loop risk. Add max_steps limit (recommended: 50).
No Execution Timeout
Severity: WARNING
Computer use should have timeout limits
Message: No timeout on execution. Add timeout (recommended: 5-10 minutes).
Container Without Memory Limit
Severity: WARNING
Containers should have memory limits to prevent DoS
Message: No memory limit on container. Add --memory 2g or similar.
No Cost Tracking
Severity: WARNING
Computer use should track API costs
Message: No cost tracking. Monitor token usage to prevent bill surprises.
No Maximum Cost Limit
Severity: INFO
Consider adding cost limits per task
Message: Consider adding max_cost_per_task to prevent expensive runaway tasks.
Collaboration
Delegation Triggers
- user needs web-only automation -> browser-automation (Playwright/Selenium more efficient for web)
- user needs security review -> security-specialist (Review sandboxing, prompt injection defenses)
- user needs container orchestration -> devops (Kubernetes, Docker Swarm for scaling)
- user needs vision model optimization -> llm-architect (Model selection, prompt engineering)
- user needs multi-agent coordination -> multi-agent-orchestration (Multiple computer use agents working together)
When to Use
- User mentions or implies: computer use
- User mentions or implies: desktop automation agent
- User mentions or implies: screen control AI
- User mentions or implies: vision-based agent
- User mentions or implies: GUI automation
- User mentions or implies: Claude computer
- User mentions or implies: OpenAI Operator
- User mentions or implies: browser agent
- User mentions or implies: visual agent
- User mentions or implies: RPA with AI
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
