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
    }

# 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

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

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"}

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()

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()

# 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"

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)

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

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

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

import random
import time

def humanized_click(x: int, y: int) -> tuple[int, int]:
    """Add human-like variance to click coordinates."""
    # Gaussian distribution around target
    # Humans typically land within ~10px of target
    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."""
    # Humans have variable reaction times
    base_delay = random.uniform(0.3, 0.8)
    # Occasionally longer pauses (reading, thinking)
    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."""
    # Bezier curve or similar
    # Humans don't move in straight lines
    steps = random.randint(10, 20)
    for i in range(steps):
        t = i / steps
        # Simple curve approximation
        x = from_pos[0] + (to_pos[0] - from_pos[0]) * t
        y = from_pos[1] + (to_pos[1] - from_pos[1]) * t
        # Add wobble
        x += random.gauss(0, 2)
        y += random.gauss(0, 2)
        pyautogui.moveTo(int(x), int(y))
        time.sleep(0.01)

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/...",
    # ... more realistic agents
]

await page.set_extra_http_headers({
    "User-Agent": random.choice(USER_AGENTS)
})

# Instead of clicking dropdown, use keyboard
async def select_dropdown_option(page, dropdown_selector, option_text):
    # Focus the dropdown
    await page.click(dropdown_selector)
    await asyncio.sleep(0.3)

    # Use keyboard to find option
    await page.keyboard.type(option_text[:3])  # Type first letters
    await asyncio.sleep(0.2)
    await page.keyboard.press("Enter")

# Instead of drag-and-drop
async def reliable_drag(page, source, target):
    # Step 1: Click and hold
    await page.mouse.move(source["x"], source["y"])
    await page.mouse.down()
    await asyncio.sleep(0.2)

    # Step 2: Move in steps
    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)

    # Step 3: Release
    await page.mouse.move(target["x"], target["y"])
    await asyncio.sleep(0.1)
    await page.mouse.up()

# If vision fails, try direct DOM manipulation
async def robust_select(page, select_selector, value):
    try:
        # Try vision approach first
        await vision_agent.select(select_selector, value)
    except Exception:
        # Fall back to direct DOM
        await page.select_option(select_selector, value=value)

async def verified_scroll(page, direction):
    # Get current scroll position
    before = await page.evaluate("window.scrollY")

    # Attempt scroll
    await page.mouse.wheel(0, 500 if direction == "down" else -500)
    await asyncio.sleep(0.3)

    # Verify it worked
    after = await page.evaluate("window.scrollY")
    if before == after:
        # Try alternative method
        await page.keyboard.press("PageDown" if direction == "down" else "PageUp")

# 1. Reduce screenshot resolution
SCREEN_SIZE = (1280, 800)  # Not 4K

# 2. Batch similar actions
# Instead of: type "hello", wait, type " world"
await page.type("hello world")

# 3. Parallelize independent tasks
# Run multiple sandboxed agents concurrently

# 4. Cache repeated computations
# If same screenshot, reuse analysis

# 5. Use smaller models for simple decisions
simple_model = "claude-haiku-..."  # For "is task done?"
complex_model = "claude-sonnet-..."  # For complex reasoning

# Estimate task duration
def estimate_duration(task_complexity: str) -> int:
    """Estimate task duration in seconds."""
    estimates = {
        "simple": 30,    # Single page, few actions
        "medium": 120,   # Multi-page, moderate actions
        "complex": 300,  # Many pages, complex interactions
    }
    return estimates.get(task_complexity, 120)

# Inform users
estimated = estimate_duration("medium")
print(f"Estimated completion: {estimated // 60}m {estimated % 60}s")

class ContextManager:
    """Manage context window usage for computer use."""

    MAX_SCREENSHOTS = 10  # Keep only recent screenshots
    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

        # Keep only recent screenshots
        if self.screenshot_count > self.MAX_SCREENSHOTS:
            self._prune_old_screenshots()

        # Store with description for context
        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:
                    # Convert to text summary
                    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."""
        # Extract any text description
        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}"
        })

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

        # Take action...

        if step % checkpoint_every == 0:
            # Create checkpoint
            context.add_checkpoint()

            # Optional: persist to disk
            save_checkpoint(context, step)

# Instead of one 50-step task:
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

class CostTracker:
    """Track and limit computer use costs."""

    # Anthropic pricing (approximate)
    INPUT_COST_PER_1K = 0.003   # Text
    OUTPUT_COST_PER_1K = 0.015
    IMAGE_COST_PER_1K = 0.01    # Roughly

    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

# Usage
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")

# 1. Lower resolution
SCREEN_SIZE = (1024, 768)  # Smaller = fewer tokens

# 2. JPEG instead of PNG (when quality ok)
screenshot.save(buffer, format="JPEG", quality=70)

# 3. Crop to relevant region
def crop_relevant(screenshot: Image, focus_area: tuple):
    """Crop to area of interest."""
    return screenshot.crop(focus_area)

# 4. Don't include screenshot every turn
if not needs_visual_update:
    # Text-only turn
    messages.append({"role": "user", "content": "Continue..."})

async def tiered_model_selection(task_complexity: str):
    """Use appropriate model for task."""
    if task_complexity == "simple":
        return "claude-haiku-..."  # Cheapest
    elif task_complexity == "medium":
        return "claude-sonnet-4-20250514"  # Balanced
    else:
        return "claude-opus-4-5-..."  # Best but expensive

# Minimum viable sandbox: Docker with restrictions

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

# Defense 1: Docker isolation
# Defense 2: Non-root user
# Defense 3: Network restrictions
# Defense 4: Filesystem restrictions
# Defense 5: Resource limits
# Defense 6: Action confirmation
# Defense 7: Action logging

@dataclass
class SandboxConfig:
    docker_image: str = "computer-use-sandbox:latest"
    network: str = "none"  # or specific allowlist
    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

class SandboxedTestRunner:
    """Run tests in throwaway containers."""

    async def run_test(self, test_task: str) -> dict:
        # Spin up fresh container
        container_id = await self.create_container()

        try:
            # Run task
            result = await self.execute_in_container(container_id, test_task)

            # Capture state for verification
            state = await self.capture_container_state(container_id)

            return {
                "result": result,
                "final_state": state,
                "logs": await self.get_logs(container_id)
            }
        finally:
            # Always destroy container
            await self.destroy_container(container_id)