# Multi-stage Dockerfile for optimized AI agent deployment
FROM python:3.9-slim as builder

# Install build dependencies
RUN apt-get update && apt-get install -y \
    gcc \
    g++ \
    && rm -rf /var/lib/apt/lists/*

# Copy requirements
COPY requirements.txt .

# Install Python dependencies in isolated environment
RUN pip install --user --no-cache-dir -r requirements.txt

# Runtime stage
FROM python:3.9-slim

# Install runtime system dependencies
RUN apt-get update && apt-get install -y \
    libgomp1 \
    && rm -rf /var/lib/apt/lists/*

# Copy Python dependencies from builder stage
COPY --from=builder /root/.local /root/.local

# Set environment variables
ENV PATH=/root/.local/bin:$PATH
ENV PYTHONUNBUFFERED=1

# Set working directory
WORKDIR /app

# Copy application code
COPY . .

# Create non-root user for security
RUN adduser --disabled-password --gecos '' agentuser
USER agentuser

# Health check for agent liveness
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
    CMD curl -f http://localhost:8000/health || exit 1

# Expose ports
EXPOSE 8000

# Entry point
ENTRYPOINT ["python", "agent.py"]

import os
import boto3
from pathlib import Path
import tarfile
import requests
from typing import Dict, Any

class ModelArtifactManager:
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.artifact_cache_dir = Path(config.get('cache_directory', '/tmp/models'))
        self.artifact_cache_dir.mkdir(parents=True, exist_ok=True)
        
    def prepare_model_artifacts(self) -> Dict[str, str]:
        """Prepare all required model artifacts for deployment"""
        artifacts = {}
        
        # Download configured artifacts
        for artifact_name, artifact_config in self.config.get('artifacts', {}).items():
            local_path = self._download_or_cache_artifact(artifact_name, artifact_config)
            artifacts[artifact_name] = str(local_path)
            
        # Validate artifact integrity
        self._validate_artifact_integrity(artifacts)
        
        return artifacts
    
    def _download_or_cache_artifact(self, name: str, config: Dict[str, Any]) -> Path:
        """Download artifact from configured source or use cached version"""
        cache_key = self._generate_cache_key(config)
        cached_path = self.artifact_cache_dir / cache_key
        
        if cached_path.exists():
            print(f"Using cached artifact: {name}")
            return cached_path
            
        # Download from source
        print(f"Downloading artifact: {name}")
        source_url = config['source_url']
        
        if source_url.startswith('s3://'):
            local_path = self._download_from_s3(source_url, cached_path)
        elif source_url.startswith('http'):
            local_path = self._download_http(source_url, cached_path)
        else:
            raise ValueError(f"Unsupported artifact source: {source_url}")
            
        # Extract if needed
        if config.get('extract', False):
            local_path = self._extract_tarball(local_path)
            
        return local_path
    
    def _download_from_s3(self, s3_url: str, local_path: Path) -> Path:
        """Download artifact from S3 bucket"""
        # Parse S3 URL
        parts = s3_url.replace('s3://', '').split('/', 1)
        bucket_name = parts[0]
        key = parts[1] if len(parts) > 1 else ''
        
        # Configure S3 client
        s3_client = boto3.client(
            's3',
            aws_access_key_id=os.getenv('AWS_ACCESS_KEY_ID'),
            aws_secret_access_key=os.getenv('AWS_SECRET_ACCESS_KEY'),
            region_name=os.getenv('AWS_REGION', 'us-east-1')
        )
        
        # Download file
        local_path.parent.mkdir(parents=True, exist_ok=True)
        s3_client.download_file(bucket_name, key, str(local_path))
        
        return local_path
    
    def _download_http(self, url: str, local_path: Path) -> Path:
        """Download artifact from HTTP(S) URL"""
        local_path.parent.mkdir(parents=True, exist_ok=True)
        
        response = requests.get(url, stream=True)
        response.raise_for_status()
        
        with open(local_path, 'wb') as f:
            for chunk in response.iter_content(chunk_size=8192):
                f.write(chunk)
                
        return local_path
    
    def _extract_tarball(self, tarball_path: Path) -> Path:
        """Extract tarball and return extracted directory path"""
        extract_dir = tarball_path.with_suffix('')
        extract_dir.mkdir(exist_ok=True)
        
        with tarfile.open(tarball_path, 'r:*') as tar:
            tar.extractall(path=extract_dir)
            
        # Clean up tarball
        tarball_path.unlink()
        
        return extract_dir
        
    def _generate_cache_key(self, config: Dict[str, Any]) -> str:
        """Generate cache key for artifact"""
        # Include version, hash, or modification date in cache key
        source_url = config['source_url']
        version = config.get('version', 'latest')
        return f"{Path(source_url).stem}_{version}".replace('/', '_')
        
    def _validate_artifact_integrity(self, artifacts: Dict[str, str]):
        """Validate downloaded artifacts for integrity"""
        for name, path in artifacts.items():
            if not Path(path).exists():
                raise RuntimeError(f"Missing artifact: {name}")
                
            # Additional validation based on configuration
            config = self.config.get('artifacts', {}).get(name, {})
            if 'checksum' in config:
                self._validate_checksum(path, config['checksum'])
                
    def _validate_checksum(self, file_path: str, expected_checksum: str):
        """Validate file checksum"""
        import hashlib
        
        with open(file_path, 'rb') as f:
            file_hash = hashlib.sha256(f.read()).hexdigest()
            
        if file_hash != expected_checksum:
            raise RuntimeError(f"Checksum mismatch for {file_path}")

# Usage example in agent initialization
def initialize_agent_with_artifacts():
    """Initialize agent with managed model artifacts"""
    config = {
        'cache_directory': '/tmp/models',
        'artifacts': {
            'language_model': {
                'source_url': 's3://my-models/language-agent-v2.tar.gz',
                'version': '2.1.0',
                'extract': True,
                'checksum': 'abc123...'
            },
            'vision_model': {
                'source_url': 'https://models.example.com/vision-agent.pb',
                'version': '1.5.2',
                'checksum': 'def456...'
            }
        }
    }
    
    artifact_manager = ModelArtifactManager(config)
    artifacts = artifact_manager.prepare_model_artifacts()
    
    # Initialize agent with loaded artifacts
    agent = Agent(language_model_path=artifacts['language_model'],
                  vision_model_path=artifacts['vision_model'])
    
    return agent

# Enhanced agent startup with artifact management
class AgentDeploymentManager:
    def __init__(self):
        self.artifact_manager = ModelArtifactManager(self._load_deployment_config())
        self.health_monitor = self._setup_health_monitoring()
        self.resource_manager = self._setup_resource_management()
        
    def start_agent_service(self):
        """Start agent service with proper initialization"""
        try:
            # Prepare required artifacts
            print("Preparing model artifacts...")
            artifacts = self.artifact_manager.prepare_model_artifacts()
            
            # Initialize agent with artifacts
            print("Initializing agent...")
            self.agent = self._create_agent_instance(artifacts)
            
            # Validate agent readiness
            print("Validating agent readiness...")
            self._validate_agent_readiness()
            
            # Start serving requests
            print("Starting agent service...")
            self._start_http_server()
            
            # Register health checks
            self.health_monitor.register_agent(self.agent)
            
        except Exception as e:
            print(f"Failed to start agent service: {e}")
            raise
            
    def _load_deployment_config(self):
        """Load deployment configuration"""
        # In practice, load from environment variables, config files, or service discovery
        return {
            'cache_directory': os.getenv('MODEL_CACHE_DIR', '/tmp/models'),
            'artifacts': {
                'main_model': {
                    'source_url': os.getenv('MODEL_URL', 's3://models/main-agent.tar.gz'),
                    'version': os.getenv('MODEL_VERSION', 'latest'),
                    'extract': True
                }
            }
        }
        
    def _create_agent_instance(self, artifacts):
        """Create agent instance with loaded artifacts"""
        # Agent-specific initialization logic
        return IntelligentAgent(
            model_path=artifacts['main_model'],
            config=self._load_agent_config()
        )
        
    def _validate_agent_readiness(self):
        """Validate that agent is ready to serve requests"""
        # Run basic functionality tests
        test_result = self.agent.run_self_test()
        if not test_result.success:
            raise RuntimeError(f"Agent self-test failed: {test_result.error}")
            
        # Validate critical pathways
        self._test_critical_functions()
        
    def _test_critical_functions(self):
        """Test agent's critical functions"""
        # Implementation depends on specific agent functionality
        pass
        
    def _setup_health_monitoring(self):
        """Setup health monitoring infrastructure"""
        return AgentHealthMonitor()
        
    def _setup_resource_management(self):
        """Setup resource management for agent"""
        return ResourceManager(
            cpu_limit=os.getenv('CPU_LIMIT', '2'),
            memory_limit=os.getenv('MEMORY_LIMIT', '4G')
        )
        
    def _start_http_server(self):
        """Start HTTP server for agent API"""
        from flask import Flask
        
        app = Flask(__name__)
        
        @app.route('/health')
        def health_check():
            return self.health_monitor.get_health_status()
            
        @app.route('/predict', methods=['POST'])
        def predict():
            # Agent inference endpoint
            return self.agent.handle_request(request.json)
            
        # Start server in background thread
        import threading
        server_thread = threading.Thread(
            target=app.run,
            kwargs={'host': '0.0.0.0', 'port': 8000, 'debug': False}
        )
        server_thread.daemon = True
        server_thread.start()

class AgentHealthMonitor:
    def __init__(self):
        self.agents = []
        self.health_checks = []
        
    def register_agent(self, agent):
        """Register agent for health monitoring"""
        self.agents.append(agent)
        self.health_checks.append({
            'last_check': None,
            'status': 'unknown',
            'details': {}
        })
        
    def get_health_status(self):
        """Get aggregated health status"""
        statuses = []
        for i, agent in enumerate(self.agents):
            status = self._check_agent_health(agent, i)
            statuses.append(status)
            
        overall_status = 'healthy' if all(s['status'] == 'healthy' for s in statuses) else 'degraded'
        
        return {
            'status': overall_status,
            'agents': statuses,
            'timestamp': self._current_timestamp()
        }
        
    def _check_agent_health(self, agent, index):
        """Check individual agent health"""
        try:
            # Perform health checks
            response_time = self._measure_response_time(agent)
            resource_usage = self._get_resource_usage()
            error_rate = self._calculate_error_rate()
            
            # Determine status based on thresholds
            if response_time > 5.0 or error_rate > 0.05:
                status = 'degraded'
            elif response_time > 10.0 or error_rate > 0.1:
                status = 'unhealthy'
            else:
                status = 'healthy'
                
            return {
                'status': status,
                'response_time': response_time,
                'resource_usage': resource_usage,
                'error_rate': error_rate
            }
            
        except Exception as e:
            return {
                'status': 'unhealthy',
                'error': str(e)
            }
            
    def _measure_response_time(self, agent):
        """Measure agent response time"""
        import time
        start_time = time.time()
        agent.run_simple_test()
        return time.time() - start_time
        
    def _get_resource_usage(self):
        """Get current resource usage"""
        import psutil
        return {
            'cpu_percent': psutil.cpu_percent(),
            'memory_percent': psutil.virtual_memory().percent,
            'disk_percent': psutil.disk_usage('/').percent
        }
        
    def _calculate_error_rate(self):
        """Calculate recent error rate"""
        # Implementation would track actual request/error counts
        return 0.0
        
    def _current_timestamp(self):
        """Get current timestamp"""
        from datetime import datetime
        return datetime.utcnow().isoformat()

# Example agent class
class IntelligentAgent:
    def __init__(self, model_path, config):
        self.model_path = model_path
        self.config = config
        self._load_model()
        
    def _load_model(self):
        """Load AI model for agent"""
        # Implementation depends on model type and framework
        print(f"Loading model from {self.model_path}")
        
    def handle_request(self, request_data):
        """Handle incoming request"""
        # Process request and generate response
        return {"result": "processed"}
        
    def run_self_test(self):
        """Run self-test to verify functionality"""
        try:
            # Simple test to verify model loading and basic operation
            self.handle_request({"test": "data"})
            return type('TestResult', (), {'success': True, 'error': None})()
        except Exception as e:
            return type('TestResult', (), {'success': False, 'error': str(e)})()
            
    def run_simple_test(self):
        """Simple test for health checking"""
        pass

# Resource management utilities
class ResourceManager:
    def __init__(self, cpu_limit, memory_limit):
        self.cpu_limit = cpu_limit
        self.memory_limit = memory_limit
        self._apply_limits()
        
    def _apply_limits(self):
        """Apply resource limits to current process"""
        # Implementation would use cgroups or similar mechanisms
        print(f"Setting CPU limit: {self.cpu_limit}, Memory limit: {self.memory_limit}")

# Usage example
if __name__ == "__main__":
    deployment_manager = AgentDeploymentManager()
    deployment_manager.start_agent_service()

# Kubernetes deployment with persistent volumes for stateful agents
apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: intelligent-agent-cluster
spec:
  serviceName: "agent-service"
  replicas: 3
  selector:
    matchLabels:
      app: intelligent-agent
  template:
    metadata:
      labels:
        app: intelligent-agent
    spec:
      containers:
      - name: agent-container
        image: mycompany/intelligent-agent:v2.1.0
        ports:
        - containerPort: 8000
          name: http
        env:
        - name: AGENT_ID
          valueFrom:
            fieldRef:
              fieldPath: metadata.name
        - name: CLUSTER_SIZE
          value: "3"
        - name: MODEL_SOURCE_URL
          value: "s3://my-models/language-agent-v2.tar.gz"
        volumeMounts:
        - name: model-cache
          mountPath: /tmp/models
        - name: agent-state
          mountPath: /var/lib/agent/state
        resources:
          requests:
            memory: "2Gi"
            cpu: "1"
          limits:
            memory: "4Gi"
            cpu: "2"
        livenessProbe:
          httpGet:
            path: /health
            port: http
          initialDelaySeconds: 60
          periodSeconds: 30
        readinessProbe:
          httpGet:
            path: /ready
            port: http
          initialDelaySeconds: 30
          periodSeconds: 10
      volumes:
      - name: model-cache
        emptyDir: {}
  volumeClaimTemplates:
  - metadata:
      name: agent-state
    spec:
      accessModes: [ "ReadWriteOnce" ]
      resources:
        requests:
          storage: 10Gi
      storageClassName: fast-ssd
---
apiVersion: v1
kind: Service
metadata:
  name: agent-service
  labels:
    app: intelligent-agent
spec:
  ports:
  - port: 8000
    name: http
  clusterIP: None
  selector:
    app: intelligent-agent
---
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: agent-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: StatefulSet
    name: intelligent-agent-cluster
  minReplicas: 3
  maxReplicas: 20
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Pods
    pods:
      metric:
        name: agent_request_queue_length
      target:
        type: AverageValue
        averageValue: "50"
  behavior:
    scaleDown:
      stabilizationWindowSeconds: 300
      policies:
      - type: Percent
        value: 10
        periodSeconds: 60
    scaleUp:
      stabilizationWindowSeconds: 60
      policies:
      - type: Percent
        value: 50
        periodSeconds: 60

import asyncio
import time
from typing import Dict, Any, List
from dataclasses import dataclass
from enum import Enum

class WorkloadType(Enum):
    IDLE = "idle"
    PROCESSING = "processing"
    LEARNING = "learning"
    COMMUNICATION = "communication"

@dataclass
class ResourceMetrics:
    cpu_utilization: float
    memory_usage: float
    gpu_utilization: float
    network_io: float
    disk_io: float
    queue_length: int

class PredictiveScalingManager:
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.scaling_policies = self._initialize_scaling_policies()
        self.prediction_models = self._load_prediction_models()
        self.historical_data = []
        
    def evaluate_scaling_needs(self, current_metrics: ResourceMetrics, 
                              agent_states: List[Dict[str, Any]]) -> Dict[str, Any]:
        """Evaluate scaling requirements based on current and predicted workload"""
        scaling_decision = {
            'scale_direction': 'maintain',
            'replica_count_change': 0,
            'reasoning': '',
            'confidence': 0.0
        }
        
        # Analyze current workload characteristics
        current_workload_pattern = self._analyze_workload_pattern(agent_states)
        
        # Predict near-term resource requirements
        predicted_metrics = self._predict_future_metrics(current_metrics, agent_states)
        
        # Evaluate against scaling policies
        policy_evaluation = self._evaluate_scaling_policies(
            current_metrics, 
            predicted_metrics, 
            current_workload_pattern
        )
        
        # Make scaling decision
        scaling_decision.update(policy_evaluation)
        
        return scaling_decision
        
    def _analyze_workload_pattern(self, agent_states: List[Dict[str, Any]]) -> Dict[str, Any]:
        """Analyze current workload pattern across agent cluster"""
        workload_analysis = {
            'processing_intensity': 0.0,
            'learning_activity': 0.0,
            'communication_load': 0.0,
            'idle_ratio': 0.0,
            'complexity_distribution': []
        }
        
        if not agent_states:
            return workload_analysis
            
        processing_agents = sum(1 for state in agent_states 
                              if state.get('current_workload') == WorkloadType.PROCESSING.value)
        learning_agents = sum(1 for state in agent_states 
                            if state.get('current_workload') == WorkloadType.LEARNING.value)
        communication_agents = sum(1 for state in agent_states 
                                 if state.get('current_workload') == WorkloadType.COMMUNICATION.value)
        
        total_agents = len(agent_states)
        
        workload_analysis['processing_intensity'] = processing_agents / total_agents
        workload_analysis['learning_activity'] = learning_agents / total_agents
        workload_analysis['communication_load'] = communication_agents / total_agents
        workload_analysis['idle_ratio'] = (total_agents - processing_agents - learning_agents - communication_agents) / total_agents
        
        # Analyze complexity distribution (if available)
        complexities = [state.get('current_complexity', 1) for state in agent_states]
        if complexities:
            workload_analysis['complexity_distribution'] = {
                'mean': sum(complexities) / len(complexities),
                'max': max(complexities),
                'min': min(complexities)
            }
            
        return workload_analysis
        
    def _predict_future_metrics(self, current_metrics: ResourceMetrics, 
                               agent_states: List[Dict[str, Any]]) -> ResourceMetrics:
        """Predict future resource metrics based on current state and trends"""
        # Simplified prediction - in practice, use ML models
        prediction_horizon = self.config.get('prediction_horizon_seconds', 300)  # 5 minutes
        
        # Estimate workload growth trends
        trend_factor = self._calculate_trend_factor()
        
        # Predict resource requirements
        predicted_cpu = min(100.0, current_metrics.cpu_utilization * (1 + trend_factor))
        predicted_memory = current_metrics.memory_usage * (1 + trend_factor * 0.5)
        predicted_queue = max(0, current_metrics.queue_length * (1 + trend_factor * 2))
        
        return ResourceMetrics(
            cpu_utilization=predicted_cpu,
            memory_usage=predicted_memory,
            gpu_utilization=current_metrics.gpu_utilization * (1 + trend_factor),
            network_io=current_metrics.network_io * (1 + trend_factor),
            disk_io=current_metrics.disk_io,
            queue_length=int(predicted_queue)
        )
        
    def _calculate_trend_factor(self) -> float:
        """Calculate trend factor based on historical data"""
        if len(self.historical_data) < 2:
            return 0.0
            
        recent_window = self.historical_data[-10:]  # Last 10 data points
        if len(recent_window) < 2:
            return 0.0
            
        # Simple linear trend calculation
        timestamps = [data['timestamp'] for data in recent_window]
        cpu_values = [data['metrics'].cpu_utilization for data in recent_window]
        
        if len(timestamps) < 2:
            return 0.0
            
        time_diff = timestamps[-1] - timestamps[0]
        cpu_diff = cpu_values[-1] - cpu_values[0]
        
        if time_diff == 0:
            return 0.0
            
        # Rate of change per minute
        rate_per_minute = (cpu_diff / time_diff) * 60
        return max(-1.0, min(1.0, rate_per_minute / 100.0))  # Normalize to [-1, 1]
        
    def _evaluate_scaling_policies(self, current: ResourceMetrics, 
                                  predicted: ResourceMetrics,
                                  workload_pattern: Dict[str, Any]) -> Dict[str, Any]:
        """Evaluate scaling policies against current and predicted metrics"""
        evaluation_results = {
            'scale_direction': 'maintain',
            'replica_count_change': 0,
            'reasoning': 'No scaling action required',
            'confidence': 0.8
        }
        
        # Check CPU-based scaling triggers
        if predicted.cpu_utilization > self.config.get('cpu_scale_up_threshold', 80):
            evaluation_results.update({
                'scale_direction': 'scale_up',
                'replica_count_change': self._calculate_replica_increase(predicted.cpu_utilization),
                'reasoning': f'Predicted CPU utilization ({predicted.cpu_utilization:.1f}%) exceeds threshold',
                'confidence': 0.9
            })
        elif current.cpu_utilization < self.config.get('cpu_scale_down_threshold', 30):
            evaluation_results.update({
                'scale_direction': 'scale_down',
                'replica_count_change': self._calculate_replica_decrease(current.cpu_utilization),
                'reasoning': f'Current CPU utilization ({current.cpu_utilization:.1f}%) below threshold',
                'confidence': 0.85
            })
            
        # Check queue-based scaling (important for agent responsiveness)
        if predicted.queue_length > self.config.get('queue_scale_up_threshold', 100):
            # Override CPU-based decision if queue is critically high
            evaluation_results.update({
                'scale_direction': 'scale_up',
                'replica_count_change': max(
                    evaluation_results['replica_count_change'],
                    self._calculate_queue_based_scaling(predicted.queue_length)
                ),
                'reasoning': f'Predicted queue length ({predicted.queue_length}) exceeds threshold',
                'confidence': 0.95
            })
            
        # Consider workload pattern for more intelligent scaling
        if workload_pattern['learning_activity'] > 0.5:
            # Scale down during heavy learning phases to avoid resource contention
            evaluation_results.update({
                'scale_direction': 'scale_down',
                'replica_count_change': min(0, evaluation_results['replica_count_change']),
                'reasoning': 'High learning activity detected - optimizing for learning efficiency',
                'confidence': 0.8
            })
            
        return evaluation_results
        
    def _calculate_replica_increase(self, cpu_utilization: float) -> int:
        """Calculate number of replicas to add based on CPU utilization"""
        base_increase = max(1, int(cpu_utilization / 20))  # Scale factor of 20%
        # Apply maximum increase limit
        max_increase = self.config.get('max_replica_increase', 5)
        return min(base_increase, max_increase)
        
    def _calculate_replica_decrease(self, cpu_utilization: float) -> int:
        """Calculate number of replicas to remove based on CPU utilization"""
        # More conservative scaling down to avoid thrashing
        base_decrease = max(1, int((30 - cpu_utilization) / 10))  # Scale factor of 10%
        max_decrease = self.config.get('max_replica_decrease', 2)
        return min(base_decrease, max_decrease)
        
    def _calculate_queue_based_scaling(self, queue_length: int) -> int:
        """Calculate scaling based on queue length"""
        # Linear scaling based on queue length excess
        threshold = self.config.get('queue_scale_up_threshold', 100)
        excess = max(0, queue_length - threshold)
        return max(1, int(excess / 50))  # Add 1 replica per 50 items over threshold
        
    def _initialize_scaling_policies(self) -> Dict[str, Any]:
        """Initialize scaling policies from configuration"""
        return {
            'cpu_based': {
                'scale_up_threshold': 80,
                'scale_down_threshold': 30,
                'cooldown_period': 300  # 5 minutes
            },
            'queue_based': {
                'scale_up_threshold': 100,
                'scale_down_threshold': 20,
                'response_time_target': 1.0  # seconds
            },
            'learning_optimized': {
                'learning_consolidation_threshold': 0.6,
                'resource_isolation_preference': True
            }
        }
        
    def _load_prediction_models(self) -> Any:
        """Load ML models for predictive scaling"""
        # In production, load trained forecasting models
        # For now, return mock predictor
        return type('PredictionModel', (), {
            'predict': lambda x: x
        })()

# Agent state monitoring component
class AgentStateCollector:
    def __init__(self):
        self.state_cache = {}
        self.collection_interval = 30  # seconds
        
    async def collect_agent_states(self, agent_endpoints: List[str]) -> List[Dict[str, Any]]:
        """Collect state information from all agents in cluster"""
        agent_states = []
        
        for endpoint in agent_endpoints:
            try:
                state = await self._fetch_agent_state(endpoint)
                agent_states.append(state)
            except Exception as e:
                print(f"Failed to collect state from {endpoint}: {e}")
                
        return agent_states
        
    async def _fetch_agent_state(self, endpoint: str) -> Dict[str, Any]:
        """Fetch state from individual agent"""
        import aiohttp
        
        async with aiohttp.ClientSession() as session:
            async with session.get(f"http://{endpoint}/state") as response:
                response.raise_for_status()
                state_data = await response.json()
                
        return {
            'endpoint': endpoint,
            'timestamp': time.time(),
            **state_data
        }
        
    def update_state_cache(self, agent_states: List[Dict[str, Any]]):
        """Update local state cache"""
        for state in agent_states:
            self.state_cache[state['endpoint']] = state
            
    def get_cached_states(self) -> List[Dict[str, Any]]:
        """Get currently cached agent states"""
        return list(self.state_cache.values())

# Integration with orchestration system
class SmartOrchestratorInterface:
    def __init__(self, kubernetes_client, scaling_manager: PredictiveScalingManager):
        self.k8s_client = kubernetes_client
        self.scaling_manager = scaling_manager
        self.state_collector = AgentStateCollector()
        
    async def manage_agent_cluster(self, namespace: str, deployment_name: str):
        """Main cluster management loop"""
        while True:
            try:
                # Collect current agent states
                agent_endpoints = self._discover_agent_endpoints(namespace, deployment_name)
                agent_states = await self.state_collector.collect_agent_states(agent_endpoints)
                self.state_collector.update_state_cache(agent_states)
                
                # Collect resource metrics
                current_metrics = self._collect_resource_metrics(namespace, deployment_name)
                
                # Evaluate scaling needs
                scaling_decision = self.scaling_manager.evaluate_scaling_needs(
                    current_metrics, agent_states
                )
                
                # Apply scaling decisions
                if scaling_decision['scale_direction'] != 'maintain':
                    self._apply_scaling_decision(namespace, deployment_name, scaling_decision)
                    
                # Store historical data for trend analysis
                self._store_historical_data(current_metrics, agent_states, scaling_decision)
                
                # Wait before next evaluation
                await asyncio.sleep(self.scaling_manager.config.get('evaluation_interval', 60))
                
            except Exception as e:
                print(f"Error in cluster management: {e}")
                await asyncio.sleep(30)  # Brief pause before retry
                
    def _discover_agent_endpoints(self, namespace: str, deployment_name: str) -> List[str]:
        """Discover endpoints of running agent instances"""
        # In practice, query Kubernetes API for pod IPs and ports
        # For demonstration, return mock endpoints
        return [f"agent-{i}.svc.cluster.local:8000" for i in range(3)]
        
    def _collect_resource_metrics(self, namespace: str, deployment_name: str) -> ResourceMetrics:
        """Collect resource metrics from monitoring system"""
        # In practice, integrate with Prometheus, Metrics API, or similar
        # Return mock metrics for demonstration
        return ResourceMetrics(
            cpu_utilization=45.0,
            memory_usage=2.5,  # GB
            gpu_utilization=15.0,
            network_io=100.0,  # Mbps
            disk_io=50.0,  # MB/s
            queue_length=35
        )
        
    def _apply_scaling_decision(self, namespace: str, deployment_name: str, 
                               decision: Dict[str, Any]):
        """Apply scaling decision to Kubernetes deployment"""
        print(f"Applying scaling decision: {decision}")
        
        # In practice, use Kubernetes Python client to update deployment
        # apps_v1.patch_namespaced_deployment(...)
        pass
        
    def _store_historical_data(self, metrics: ResourceMetrics, 
                              states: List[Dict[str, Any]], 
                              decision: Dict[str, Any]):
        """Store data for predictive analysis"""
        self.scaling_manager.historical_data.append({
            'timestamp': time.time(),
            'metrics': metrics,
            'agent_states': states,
            'scaling_decision': decision
        })
        
        # Keep only recent history
        max_history = 1000
        if len(self.scaling_manager.historical_data) > max_history:
            self.scaling_manager.historical_data = self.scaling_manager.historical_data[-max_history:]

# Example usage
async def main():
    # Initialize components
    scaling_manager = PredictiveScalingManager({
        'cpu_scale_up_threshold': 75,
        'cpu_scale_down_threshold': 25,
        'queue_scale_up_threshold': 50,
        'max_replica_increase': 3,
        'max_replica_decrease': 1,
        'evaluation_interval': 45
    })
    
    orchestrator = SmartOrchestratorInterface(None, scaling_manager)  # Mock Kubernetes client
    
    # Start cluster management
    await orchestrator.manage_agent_cluster("production", "intelligent-agent-cluster")

# Run example
# asyncio.run(main())

import json
import time
import uuid
from typing import Dict, Any, List, Optional
from dataclasses import dataclass, asdict
from datetime import datetime
import asyncio
import traceback
from collections import defaultdict
import numpy as np

@dataclass
class ObservationEvent:
    """Base structure for agent observation events"""
    event_id: str
    timestamp: float
    event_type: str
    source_agent: str
    correlation_id: Optional[str]
    payload: Dict[str, Any]
    metadata: Dict[str, Any]

class AgentObservabilityStack:
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.event_collectors = self._initialize_event_collectors()
        self.analyzers = self._initialize_analyzers()
        self.alerting_system = self._initialize_alerting_system()
        self.storage_backend = self._initialize_storage_backend()
        
    def record_logic_event(self, agent_id: str, decision_info: Dict[str, Any], 
                          context: Dict[str, Any]) -> str:
        """Record a logical decision made by the agent"""
        event = ObservationEvent(
            event_id=str(uuid.uuid4()),
            timestamp=time.time(),
            event_type="logic_decision",
            source_agent=agent_id,
            correlation_id=context.get('request_id'),
            payload={
                "decision": decision_info,
                "context": context,
                "reasoning_chain": self._capture_reasoning_traceback()
            },
            metadata={
                "agent_version": self._get_agent_version(),
                "environment": self._get_environment_info(),
                "severity": "INFO"
            }
        )
        
        self._store_event(event)
        self._trigger_analyzers(event)
        
        return event.event_id
        
    def record_learning_event(self, agent_id: str, learning_info: Dict[str, Any]) -> str:
        """Record a learning adaptation event"""
        event = ObservationEvent(
            event_id=str(uuid.uuid4()),
            timestamp=time.time(),
            event_type="learning_adaptation",
            source_agent=agent_id,
            correlation_id=None,
            payload=learning_info,
            metadata={
                "agent_version": self._get_agent_version(),
                "environment": self._get_environment_info(),
                "learning_cycle": self._get_learning_cycle_count(),
                "severity": "DEBUG"
            }
        )
        
        self._store_event(event)
        return event.event_id
        
    def record_interaction_event(self, agent_id: str, interaction_info: Dict[str, Any],
                                related_agents: List[str] = None) -> str:
        """Record an interaction with external systems or other agents"""
        event = ObservationEvent(
            event_id=str(uuid.uuid4()),
            timestamp=time.time(),
            event_type="agent_interaction",
            source_agent=agent_id,
            correlation_id=interaction_info.get('session_id'),
            payload=interaction_info,
            metadata={
                "related_agents": related_agents or [],
                "agent_version": self._get_agent_version(),
                "interaction_type": interaction_info.get('type', 'unknown'),
                "severity": "INFO"
            }
        )
        
        self._store_event(event)
        return event.event_id
        
    def record_error_event(self, agent_id: str, error_info: Dict[str, Any],
                          stack_trace: str = None) -> str:
        """Record an error or exceptional condition"""
        event = ObservationEvent(
            event_id=str(uuid.uuid4()),
            timestamp=time.time(),
            event_type="agent_error",
            source_agent=agent_id,
            correlation_id=error_info.get('request_id'),
            payload={
                "error": error_info,
                "stack_trace": stack_trace or traceback.format_exc()
            },
            metadata={
                "agent_version": self._get_agent_version(),
                "environment": self._get_environment_info(),
                "severity": error_info.get('severity', 'ERROR'),
                "recovered": error_info.get('recovered', False)
            }
        )
        
        self._store_event(event)
        self._trigger_alerting(event)
        
        return event.event_id
        
    def _store_event(self, event: ObservationEvent):
        """Store event in backend storage"""
        self.storage_backend.store_event(asdict(event))
        
    def _trigger_analyzers(self, event: ObservationEvent):
        """Trigger analyzers relevant to event type"""
        for analyzer in self.analyzers:
            if analyzer.applies_to_event(event):
                asyncio.create_task(analyzer.analyze(event))
                
    def _trigger_alerting(self, event: ObservationEvent):
        """Trigger alerting system for critical events"""
        if event.metadata.get('severity') in ['ERROR', 'CRITICAL']:
            self.alerting_system.send_alert(event)
            
    def _capture_reasoning_traceback(self) -> List[Dict[str, Any]]:
        """Capture the current reasoning chain (conceptually)"""
        # In practice, this would hook into the agent's reasoning framework
        # to capture decision tracebacks
        return [
            {
                "step": "initial_observation",
                "description": "Observed environmental state"
            },
            {
                "step": "hypothesis_generation",
                "description": "Generated potential action hypotheses"
            },
            {
                "step": "evaluation",
                "description": "Evaluated hypotheses against internal models"
            },
            {
                "step": "selection",
                "description": "Selected optimal action based on evaluation"
            }
        ]
        
    def _get_agent_version(self) -> str:
        """Get current agent version"""
        # Implementation depends on versioning system
        return "2.1.0"
        
    def _get_environment_info(self) -> Dict[str, str]:
        """Get current environment information"""
        import os
        return {
            "deployment_env": os.getenv("DEPLOYMENT_ENV", "development"),
            "region": os.getenv("REGION", "us-east-1"),
            "instance_id": os.getenv("INSTANCE_ID", "unknown")
        }
        
    def _get_learning_cycle_count(self) -> int:
        """Get current learning cycle count"""
        # Implementation depends on agent's learning tracking
        return 1250

# Event collector implementations
class EventCollector:
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        
    def collect_events(self) -> List[ObservationEvent]:
        """Collect events from source"""
        raise NotImplementedError

class LogBasedCollector(EventCollector):
    def __init__(self, config: Dict[str, Any]):
        super().__init__(config)
        self.log_paths = config.get('log_paths', [])
        
    def collect_events(self) -> List[ObservationEvent]:
        """Collect events from structured logs"""
        events = []
        # Implementation would parse log files and convert to events
        return events

class APICollector(EventCollector):
    def __init__(self, config: Dict[str, Any]):
        super().__init__(config)
        self.api_endpoints = config.get('endpoints', [])
        
    def collect_events(self) -> List[ObservationEvent]:
        """Collect events via API polling"""
        events = []
        # Implementation would poll agent APIs for state information
        return events

class StreamCollector(EventCollector):
    def __init__(self, config: Dict[str, Any]):
        super().__init__(config)
        self.stream_sources = config.get('sources', [])
        
    def collect_events(self) -> List[ObservationEvent]:
        """Collect events from streaming sources"""
        events = []
        # Implementation would consume message queues or streaming platforms
        return events

# Analyzer implementations
class EventAnalyzer:
    def applies_to_event(self, event: ObservationEvent) -> bool:
        """Check if analyzer should handle this event type"""
        raise NotImplementedError
        
    async def analyze(self, event: ObservationEvent):
        """Perform analysis on event"""
        raise NotImplementedError

class AnomalyDetector(EventAnalyzer):
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.baseline_metrics = self._load_baseline_metrics()
        self.anomaly_threshold = config.get('threshold', 2.0)
        
    def applies_to_event(self, event: ObservationEvent) -> bool:
        return event.event_type in ['logic_decision', 'agent_error']
        
    async def analyze(self, event: ObservationEvent):
        """Analyze event for anomalous behavior"""
        if self._is_anomalous(event):
            await self._report_anomaly(event)
            
    def _is_anomalous(self, event: ObservationEvent) -> bool:
        """Detect if event represents anomalous behavior"""
        # Statistical analysis of decision patterns
        if event.event_type == 'logic_decision':
            decision_metrics = self._extract_decision_metrics(event.payload)
            return self._detect_statistical_anomaly(decision_metrics)
        elif event.event_type == 'agent_error':
            return self._detect_error_spike(event)
            
        return False
        
    def _extract_decision_metrics(self, decision_payload: Dict[str, Any]) -> Dict[str, float]:
        """Extract quantitative metrics from decision payload"""
        return {
            'complexity_score': decision_payload.get('decision', {}).get('complexity', 0),
            'execution_time': decision_payload.get('decision', {}).get('execution_time', 0),
            'confidence_level': decision_payload.get('decision', {}).get('confidence', 0)
        }
        
    def _detect_statistical_anomaly(self, metrics: Dict[str, float]) -> bool:
        """Detect statistical anomalies in metrics"""
        for metric_name, metric_value in metrics.items():
            if metric_name in self.baseline_metrics:
                baseline = self.baseline_metrics[metric_name]
                z_score = abs(metric_value - baseline['mean']) / baseline['std']
                if z_score > self.anomaly_threshold:
                    return True
                    
        return False
        
    def _detect_error_spike(self, event: ObservationEvent) -> bool:
        """Detect sudden spikes in error rates"""
        # Implementation would check historical error rates
        return False  # Placeholder
        
    async def _report_anomaly(self, event: ObservationEvent):
        """Report detected anomaly"""
        print(f"ANOMALY DETECTED: {event.event_type} from {event.source_agent}")
        # In practice, send to monitoring dashboard or alerting system

class PerformanceAnalyzer(EventAnalyzer):
    def applies_to_event(self, event: ObservationEvent) -> bool:
        return event.event_type in ['logic_decision', 'agent_interaction']
        
    async def analyze(self, event: ObservationEvent):
        """Analyze performance characteristics"""
        performance_metrics = self._calculate_performance_metrics(event)
        self._update_performance_dashboards(performance_metrics)
        
    def _calculate_performance_metrics(self, event: ObservationEvent) -> Dict[str, Any]:
        """Calculate performance-related metrics from event"""
        metrics = {
            'event_type': event.event_type,
            'timestamp': event.timestamp,
            'agent_id': event.source_agent
        }
        
        if event.event_type == 'logic_decision':
            decision = event.payload.get('decision', {})
            metrics.update({
                'decision_time_ms': decision.get('execution_time', 0) * 1000,
                'confidence_score': decision.get('confidence', 0),
                'complexity_score': decision.get('complexity', 0)
            })
            
        elif event.event_type == 'agent_interaction':
            interaction = event.payload
            metrics.update({
                'interaction_duration_ms': interaction.get('duration', 0) * 1000,
                'interaction_type': interaction.get('type', 'unknown'),
                'success_rate': 1.0 if interaction.get('successful', True) else 0.0
            })
            
        return metrics
        
    def _update_performance_dashboards(self, metrics: Dict[str, Any]):
        """Update performance monitoring dashboards"""
        # In practice, push metrics to monitoring systems like Prometheus/Grafana
        pass

class LearningProgressAnalyzer(EventAnalyzer):
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.learning_history = defaultdict(list)
        
    def applies_to_event(self, event: ObservationEvent) -> bool:
        return event.event_type == 'learning_adaptation'
        
    async def analyze(self, event: ObservationEvent):
        """Analyze learning progress"""
        agent_id = event.source_agent
        learning_info = event.payload
        
        # Store learning event for agent
        self.learning_history[agent_id].append({
            'timestamp': event.timestamp,
            'improvement': learning_info.get('improvement_score', 0),
            'knowledge_acquired': learning_info.get('new_knowledge', []),
            'cycle_duration': learning_info.get('cycle_duration', 0)
        })
        
        # Analyze overall learning progress
        progress_metrics = self._calculate_learning_progress(agent_id)
        self._update_learning_dashboards(progress_metrics)
        
    def _calculate_learning_progress(self, agent_id: str) -> Dict[str, Any]:
        """Calculate learning progress metrics for agent"""
        history = self.learning_history[agent_id]
        if not history:
            return {}
            
        improvements = [entry['improvement'] for entry in history]
        cycle_times = [entry['cycle_duration'] for entry in history]
        
        return {
            'agent_id': agent_id,
            'total_cycles': len(history),
            'average_improvement': np.mean(improvements) if improvements else 0,
            'improvement_trend': self._calculate_trend(improvements),
            'average_cycle_time': np.mean(cycle_times) if cycle_times else 0,
            'recent_activity': time.time() - history[-1]['timestamp']
        }
        
    def _calculate_trend(self, values: List[float]) -> float:
        """Calculate trend direction for values"""
        if len(values) < 2:
            return 0
            
        # Simple linear regression slope
        x = np.arange(len(values))
        slope = np.polyfit(x, values, 1)[0]
        return slope
        
    def _update_learning_dashboards(self, metrics: Dict[str, Any]):
        """Update learning progress dashboards"""
        # Push learning metrics to dashboards
        print(f"Learning Progress Update: {metrics}")

# Alerting system
class AlertingSystem:
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.alert_channels = self._initialize_alert_channels()
        self.alert_suppressions = self._load_alert_suppressions()
        
    def send_alert(self, event: ObservationEvent):
        """Send alert based on event"""
        alert_config = self._resolve_alert_configuration(event)
        if not self._should_send_alert(event, alert_config):
            return
            
        alert_message = self._format_alert_message(event, alert_config)
        
        for channel in self.alert_channels:
            if channel.supports_severity(alert_config.get('severity')):
                channel.send(alert_message)
                
    def _resolve_alert_configuration(self, event: ObservationEvent) -> Dict[str, Any]:
        """Resolve alert configuration for event"""
        # Logic to determine appropriate alerting rules based on event type and severity
        return {
            'severity': event.metadata.get('severity', 'INFO'),
            'channels': ['slack', 'email'],
            'suppression_window': 300  # 5 minutes
        }
        
    def _should_send_alert(self, event: ObservationEvent, config: Dict[str, Any]) -> bool:
        """Determine if alert should be sent based on suppression rules"""
        alert_key = f"{event.source_agent}:{event.event_type}"
        last_alert_time = self.alert_suppressions.get(alert_key, 0)
        
        suppression_window = config.get('suppression_window', 300)
        if time.time() - last_alert_time < suppression_window:
            return False
            
        # Update suppression time
        self.alert_suppressions[alert_key] = time.time()
        return True
        
    def _format_alert_message(self, event: ObservationEvent, config: Dict[str, Any]) -> str:
        """Format alert message for delivery"""
        timestamp = datetime.fromtimestamp(event.timestamp).strftime('%Y-%m-%d %H:%M:%S')
        
        message = (
            f"🚨 Agent Alert [{config['severity']}]\n"
            f"Time: {timestamp}\n"
            f"Agent: {event.source_agent}\n"
            f"Event: {event.event_type}\n"
            f"Details: {json.dumps(event.payload, indent=2)}"
        )
        
        return message

# Storage backend interface
class StorageBackend:
    def store_event(self, event_data: Dict[str, Any]):
        """Store event data"""
        raise NotImplementedError

class ElasticsearchStorage(StorageBackend):
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.elasticsearch_client = self._connect_elasticsearch()
        
    def store_event(self, event_data: Dict[str, Any]):
        """Store event in Elasticsearch"""
        index_name = f"agent-events-{datetime.now().strftime('%Y.%m.%d')}"
        self.elasticsearch_client.index(index=index_name, body=event_data)

class TimescaleDBStorage(StorageBackend):
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.connection = self._connect_database()
        
    def store_event(self, event_data: Dict[str, Any]):
        """Store event in TimescaleDB"""
        # Insert event data with proper schema mapping
        pass

# Integration with real-world agent systems
class ObservabilityIntegration:
    def __init__(self, observability_stack: AgentObservabilityStack):
        self.observability = observability_stack
        
    def instrument_agent_class(self, agent_class):
        """Instrument agent class with observability hooks"""
        original_decide = agent_class.decide
        original_learn = agent_class.learn
        original_interact = agent_class.interact
        
        def instrumented_decide(self, context):
            start_time = time.time()
            try:
                result = original_decide(self, context)
                
                # Record successful decision
                decision_duration = time.time() - start_time
                self.observability.record_logic_event(
                    agent_id=getattr(self, 'agent_id', 'unknown'),
                    decision_info={
                        'action': result.action if hasattr(result, 'action') else str(result),
                        'confidence': getattr(result, 'confidence', 1.0),
                        'complexity': getattr(result, 'complexity', 0),
                        'execution_time': decision_duration
                    },
                    context=context
                )
                
                return result
                
            except Exception as e:
                # Record error
                self.observability.record_error_event(
                    agent_id=getattr(self, 'agent_id', 'unknown'),
                    error_info={
                        'operation': 'decision_making',
                        'error_type': type(e).__name__,
                        'error_message': str(e),
                        'context': context,
                        'severity': 'ERROR'
                    },
                    stack_trace=traceback.format_exc()
                )
                raise
                
        def instrumented_learn(self, experience):
            start_time = time.time()
            try:
                result = original_learn(self, experience)
                
                # Record learning event
                learning_duration = time.time() - start_time
                self.observability.record_learning_event(
                    agent_id=getattr(self, 'agent_id', 'unknown'),
                    learning_info={
                        'experience_type': type(experience).__name__,
                        'improvement_score': getattr(result, 'improvement', 0),
                        'new_knowledge': getattr(result, 'acquired_knowledge', []),
                        'cycle_duration': learning_duration,
                        'success': True
                    }
                )
                
                return result
                
            except Exception as e:
                # Record learning error
                self.observability.record_error_event(
                    agent_id=getattr(self, 'agent_id', 'unknown'),
                    error_info={
                        'operation': 'learning_process',
                        'error_type': type(e).__name__,
                        'error_message': str(e),
                        'severity': 'WARNING'
                    }
                )
                return None  # Graceful degradation in learning
                
        def instrumented_interact(self, interaction_data):
            start_time = time.time()
            try:
                result = original_interact(self, interaction_data)
                
                # Record interaction event
                interaction_duration = time.time() - start_time
                self.observability.record_interaction_event(
                    agent_id=getattr(self, 'agent_id', 'unknown'),
                    interaction_info={
                        'type': interaction_data.get('type', 'unknown'),
                        'participant': interaction_data.get('with', 'unknown'),
                        'duration': interaction_duration,
                        'successful': True,
                        'outcome': str(result)[:100]  # Truncate for storage
                    },
                    related_agents=[interaction_data.get('with')]
                )
                
                return result
                
            except Exception as e:
                # Record interaction error
                self.observability.record_error_event(
                    agent_id=getattr(self, 'agent_id', 'unknown'),
                    error_info={
                        'operation': 'agent_interaction',
                        'error_type': type(e).__name__,
                        'error_message': str(e),
                        'interaction_partner': interaction_data.get('with'),
                        'severity': 'ERROR'
                    }
                )
                raise
                
        # Replace original methods with instrumented versions
        agent_class.decide = instrumented_decide
        agent_class.learn = instrumented_learn
        agent_class.interact = instrumented_interact
        
        # Inject observability reference
        agent_class.observability = self.observability
        
        return agent_class

# Example usage demonstrating full observability stack
class SampleIntelligentAgent:
    def __init__(self, agent_id: str):
        self.agent_id = agent_id
        self.experience_bank = []
        self.current_goal = None
        self.knowledge_graph = {}
        
    def decide(self, context: Dict[str, Any]):
        """Make a decision based on context"""
        # Simulate complex decision making process
        time.sleep(0.1)  # Simulate processing time
        
        # Mock decision result
        possible_actions = ['explore', 'exploit', 'learn', 'communicate', 'wait']
        chosen_action = np.random.choice(possible_actions)
        
        return type('DecisionResult', (), {
            'action': chosen_action,
            'confidence': np.random.uniform(0.6, 0.95),
            'complexity': np.random.randint(1, 10)
        })()
        
    def learn(self, experience):
        """Learn from experience"""
        self.experience_bank.append(experience)
        
        # Simulate learning improvement
        improvement = np.random.uniform(0.01, 0.1)
        
        return type('LearningResult', (), {
            'improvement': improvement,
            'acquired_knowledge': [f"pattern_{len(self.experience_bank)}"]
        })()
        
    def interact(self, interaction_data: Dict[str, Any]):
        """Interact with other agents or systems"""
        # Simulate interaction
        time.sleep(0.05)
        
        return f"Acknowledged message from {interaction_data.get('with')}"

# Demonstration setup
def setup_observability_demo():
    """Setup and demonstrate observability stack"""
    # Initialize observability stack
    config = {
        'storage': {'type': 'elasticsearch', 'host': 'localhost:9200'},
        'analyzers': ['anomaly_detector', 'performance_analyzer', 'learning_analyzer'],
        'alerting': {'channels': ['console', 'file']}
    }
    
    observability_stack = AgentObservabilityStack(config)
    
    # Instrument agent class
    integration = ObservabilityIntegration(observability_stack)
    InstrumentedAgent = integration.instrument_agent_class(SampleIntelligentAgent)
    
    # Create instrumented agent instance
    agent = InstrumentedAgent(agent_id="demo_agent_001")
    
    # Simulate agent operations that will be automatically tracked
    context = {"environment_state": "dynamic", "available_actions": ["move", "observe", "analyze"]}
    decision_result = agent.decide(context)
    
    experience = {"situation": "novel_pattern_recognition", "outcome": "success"}
    learning_result = agent.learn(experience)
    
    interaction_data = {"type": "collaboration_request", "with": "partner_agent_002"}
    interaction_result = agent.interact(interaction_data)
    
    print("Agent operations completed with automatic observability tracking!")
    print(f"Decision: {decision_result.action} (confidence: {decision_result.confidence:.2f})")
    print(f"Learning: Improvement of {learning_result.improvement:.3f}")
    print(f"Interaction: {interaction_result}")

# Run demonstration
# setup_observability_demo()

import hashlib
import hmac
import json
import time
from typing import Dict, Any, List, Optional
from dataclasses import dataclass
from cryptography.fernet import Fernet
from cryptography.hazmat.primitives import hashes
from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC
import base64
import os
from enum import Enum

class SecurityPolicy(Enum):
    STRICT = "strict"
    MODERATE = "moderate"
    PERMISSIVE = "permissive"

@dataclass
class AccessRequest:
    """Structure for access control requests"""
    resource: str
    action: str
    context: Dict[str, Any]
    timestamp: float
    agent_id: str
    request_signature: Optional[str] = None

@dataclass
class AttestationReport:
    """Agent attestation report containing security status"""
    agent_id: str
    timestamp: float
    integrity_hash: str
    runtime_state: Dict[str, Any]
    security_policy: SecurityPolicy
    allowed_operations: List[str]
    expiration_time: float

class SecureAgentRuntime:
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.security_policy = self._parse_security_policy()
        self.encryption_key = self._derive_encryption_key()
        self.access_control = self._initialize_access_control()
        self.attestation_service = self._initialize_attestation_service()
        self.audit_logger = self._initialize_audit_logging()
        
    def request_access(self, resource: str, action: str, context: Dict[str, Any]) -> bool:
        """Request access to protected resource with context validation"""
        agent_id = self._get_current_agent_id()
        timestamp = time.time()
        
        access_request = AccessRequest(
            resource=resource,
            action=action,
            context=context,
            timestamp=timestamp,
            agent_id=agent_id
        )
        
        # Sign request with agent credentials
        access_request.request_signature = self._sign_access_request(access_request)
        
        # Validate access request
        authorized = self.access_control.validate_request(access_request)
        
        # Log access attempt
        self.audit_logger.log_access_attempt(access_request, authorized)
        
        if not authorized:
            self._handle_access_denial(access_request)
            return False
            
        return True
        
    def secure_store_sensitive_data(self, key: str, data: Any) -> bool:
        """Securely store sensitive data with encryption"""
        try:
            # Serialize data
            serialized_data = json.dumps(data).encode()
            
            # Encrypt data
            fernet = Fernet(self.encryption_key)
            encrypted_data = fernet.encrypt(serialized_data)
            
            # Store with metadata
            storage_entry = {
                'encrypted_data': base64.b64encode(encrypted_data).decode(),
                'timestamp': time.time(),
                'agent_id': self._get_current_agent_id(),
                'data_hash': self._calculate_data_hash(serialized_data)
            }
            
            # Use secure storage backend
            return self._secure_storage_put(key, storage_entry)
            
        except Exception as e:
            self.audit_logger.log_security_event('data_storage_error', {
                'key': key,
                'error': str(e),
                'agent_id': self._get_current_agent_id()
            })
            return False
            
    def secure_retrieve_sensitive_data(self, key: str) -> Optional[Any]:
        """Securely retrieve encrypted sensitive data"""
        try:
            # Retrieve encrypted data
            storage_entry = self._secure_storage_get(key)
            if not storage_entry:
                return None
                
            # Verify data integrity
            stored_data = base64.b64decode(storage_entry['encrypted_data'])
            computed_hash = self._calculate_data_hash(stored_data)
            
            if computed_hash != storage_entry['data_hash']:
                self.audit_logger.log_security_event('data_integrity_violation', {
                    'key': key,
                    'agent_id': self._get_current_agent_id()
                })
                return None
                
            # Decrypt data
            fernet = Fernet(self.encryption_key)
            decrypted_data = fernet.decrypt(stored_data)
            
            # Deserialize and return
            return json.loads(decrypted_data.decode())
            
        except Exception as e:
            self.audit_logger.log_security_event('data_retrieval_error', {
                'key': key,
                'error': str(e),
                'agent_id': self._get_current_agent_id()
            })
            return None
            
    def generate_attestation_report(self) -> AttestationReport:
        """Generate security attestation report for agent"""
        agent_id = self._get_current_agent_id()
        timestamp = time.time()
        
        # Calculate runtime integrity hash
        integrity_components = [
            self._get_agent_code_hash(),
            self._get_runtime_configuration_hash(),
            self._get_active_policy_hash()
        ]
        
        integrity_hash = self._calculate_combined_hash(integrity_components)
        
        # Get current runtime state (filtered for security-relevant info only)
        runtime_state = self._capture_runtime_state()
        
        # Determine allowed operations based on current security posture
        allowed_ops = self._determine_allowed_operations()
        
        return AttestationReport(
            agent_id=agent_id,
            timestamp=timestamp,
            integrity_hash=integrity_hash,
            runtime_state=runtime_state,
            security_policy=self.security_policy,
            allowed_operations=allowed_ops,
            expiration_time=timestamp + self.config.get('attestation_validity_period', 3600)
        )
        
    def validate_peer_attestation(self, attestation: AttestationReport) -> bool:
        """Validate attestation report from peer agent"""
        # Check expiration
        if attestation.expiration_time < time.time():
            return False
            
        # Recalculate integrity hash
        integrity_components = [
            attestation.runtime_state.get('code_hash', ''),
            attestation.runtime_state.get('config_hash', ''),
            self._calculate_data_hash(attestation.runtime_state.get('policy', '').encode())
        ]
        
        calculated_hash = self._calculate_combined_hash(integrity_components)
        
        # Verify integrity
        if calculated_hash != attestation.integrity_hash:
            self.audit_logger.log_security_event('peer_attestation_failed', {
                'peer_id': attestation.agent_id,
                'reason': 'integrity_mismatch'
            })
            return False
            
        # Check policy compatibility
        if not self._are_policies_compatible(attestation.security_policy):
            self.audit_logger.log_security_event('peer_attestation_failed', {
                'peer_id': attestation.agent_id,
                'reason': 'policy_incompatible'
            })
            return False
            
        return True
        
    def _parse_security_policy(self) -> SecurityPolicy:
        """Parse security policy from configuration"""
        policy_name = self.config.get('security_policy', 'MODERATE')
        try:
            return SecurityPolicy[policy_name.upper()]
        except KeyError:
            return SecurityPolicy.MODERATE
            
    def _derive_encryption_key(self) -> bytes:
        """Derive encryption key from configuration"""
        password = self.config.get('encryption_password', 'default_secret').encode()
        salt = base64.b64decode(self.config.get('encryption_salt', 'AAAAAAAAAAAAAAAAAAAAAA=='))
        
        kdf = PBKDF2HMAC(
            algorithm=hashes.SHA256(),
            length=32,
            salt=salt,
            iterations=100000,
        )
        
        key = base64.urlsafe_b64encode(kdf.derive(password))
        return key
        
    def _sign_access_request(self, request: AccessRequest) -> str:
        """Sign access request with HMAC"""
        # Create signing string from request components
        signing_string = f"{request.agent_id}|{request.resource}|{request.action}|{request.timestamp}"
        
        # Sign with agent secret key
        secret_key = self.config.get('agent_secret_key', 'agent_secret').encode()
        signature = hmac.new(secret_key, signing_string.encode(), hashlib.sha256).hexdigest()
        
        return signature
        
    def _calculate_data_hash(self, data: bytes) -> str:
        """Calculate SHA-256 hash of data"""
        return hashlib.sha256(data).hexdigest()
        
    def _calculate_combined_hash(self, components: List[str]) -> str:
        """Calculate combined hash of multiple components"""
        combined = '|'.join(components)
        return hashlib.sha256(combined.encode()).hexdigest()
        
    def _get_current_agent_id(self) -> str:
        """Get current agent identifier"""
        return os.getenv('AGENT_ID', 'unknown_agent')
        
    def _get_agent_code_hash(self) -> str:
        """Get hash of agent executable code"""
        # In practice, hash the actual agent code or binary
        return hashlib.sha256(b"agent_code_placeholder").hexdigest()
        
    def _get_runtime_configuration_hash(self) -> str:
        """Get hash of current runtime configuration"""
        config_str = json.dumps(self.config, sort_keys=True)
        return hashlib.sha256(config_str.encode()).hexdigest()
        
    def _get_active_policy_hash(self) -> str:
        """Get hash of currently active security policy"""
        policy_str = self.security_policy.value
        return hashlib.sha256(policy_str.encode()).hexdigest()
        
    def _capture_runtime_state(self) -> Dict[str, Any]:
        """Capture security-relevant runtime state"""
        return {
            'code_hash': self._get_agent_code_hash(),
            'config_hash': self._get_runtime_configuration_hash(),
            'policy': self.security_policy.value,
            'uptime': time.time() - self.config.get('startup_time', time.time()),
            'memory_usage': self._get_memory_usage(),
            'active_connections': self._get_active_connection_count()
        }
        
    def _determine_allowed_operations(self) -> List[str]:
        """Determine allowed operations based on security policy"""
        policy_operations = {
            SecurityPolicy.STRICT: ['read_local', 'write_local'],
            SecurityPolicy.MODERATE: ['read_local', 'write_local', 'network_read'],
            SecurityPolicy.PERMISSIVE: ['read_local', 'write_local', 'network_read', 'network_write']
        }
        
        return policy_operations.get(self.security_policy, policy_operations[SecurityPolicy.MODERATE])
        
    def _are_policies_compatible(self, peer_policy: SecurityPolicy) -> bool:
        """Check if peer policy is compatible with local policy"""
        # Only allow connections with equally or more restrictive policies
        policy_hierarchy = {
            SecurityPolicy.STRICT: 3,
            SecurityPolicy.MODERATE: 2,
            SecurityPolicy.PERMISSIVE: 1
        }
        
        local_level = policy_hierarchy.get(self.security_policy, 2)
        peer_level = policy_hierarchy.get(peer_policy, 2)
        
        return peer_level >= local_level
        
    def _secure_storage_put(self, key: str, data: Dict[str, Any]) -> bool:
        """Securely store data in protected storage"""
        # Implementation would use secure storage backend (vault, encrypted database, etc.)
        print(f"Storing secure data for key: {key}")
        return True  # Mock implementation
        
    def _secure_storage_get(self, key: str) -> Optional[Dict[str, Any]]:
        """Securely retrieve data from protected storage"""
        # Implementation would use secure storage backend
        print(f"Retrieving secure data for key: {key}")
        return None  # Mock implementation
        
    def _get_memory_usage(self) -> float:
        """Get current memory usage percentage"""
        import psutil
        return psutil.virtual_memory().percent
        
    def _get_active_connection_count(self) -> int:
        """Get count of active network connections"""
        import psutil
        return len(psutil.net_connections())

# Access control service
class AccessControlService:
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.policy_engine = self._initialize_policy_engine()
        self.identity_provider = self._initialize_identity_provider()
        self.rate_limiter = self._initialize_rate_limiter()
        
    def validate_request(self, request: AccessRequest) -> bool:
        """Validate access request against security policies"""
        # Verify request signature
        if not self._verify_request_signature(request):
            return False
            
        # Check rate limiting
        if not self.rate_limiter.is_allowed(request.agent_id, request.resource):
            return False
            
        # Validate identity
        if not self.identity_provider.verify_identity(request.agent_id):
            return False
            
        # Check policy rules
        policy_decision = self.policy_engine.evaluate_request(request)
        return policy_decision.allowed
        
    def _verify_request_signature(self, request: AccessRequest) -> bool:
        """Verify HMAC signature on access request"""
        # Reconstruct signing string
        signing_string = f"{request.agent_id}|{request.resource}|{request.action}|{request.timestamp}"
        
        # Get agent's public key or shared secret
        agent_secret = self.identity_provider.get_agent_secret(request.agent_id)
        if not agent_secret:
            return False
            
        # Calculate expected signature
        expected_signature = hmac.new(
            agent_secret.encode(), 
            signing_string.encode(), 
            hashlib.sha256
        ).hexdigest()
        
        # Compare signatures (timing-safe comparison)
        return hmac.compare_digest(expected_signature, request.request_signature or '')

# Policy enforcement engine
class PolicyEngine:
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.policies = self._load_policies()
        
    def evaluate_request(self, request: AccessRequest) -> 'PolicyDecision':
        """Evaluate access request against loaded policies"""
        for policy in self.policies:
            if policy.applies_to_request(request):
                decision = policy.evaluate(request)
                if decision.final:
                    return decision
                    
        # Default deny if no applicable policies
        return PolicyDecision(allowed=False, reason="no_applicable_policy", final=True)

class Policy:
    def applies_to_request(self, request: AccessRequest) -> bool:
        """Check if policy applies to access request"""
        raise NotImplementedError
        
    def evaluate(self, request: AccessRequest) -> 'PolicyDecision':
        """Evaluate request against policy rules"""
        raise NotImplementedError

class PolicyDecision:
    def __init__(self, allowed: bool, reason: str, final: bool = False):
        self.allowed = allowed
        self.reason = reason
        self.final = final

# Identity provider for agent authentication
class IdentityProvider:
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.trusted_agents = self._load_trusted_agents()
        
    def verify_identity(self, agent_id: str) -> bool:
        """Verify agent identity"""
        return agent_id in self.trusted_agents
        
    def get_agent_secret(self, agent_id: str) -> Optional[str]:
        """Get agent's shared secret for signature verification"""
        return self.trusted_agents.get(agent_id, {}).get('secret')

# Rate limiter to prevent abuse
class RateLimiter:
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.request_counts = {}
        self.time_windows = {}
        
    def is_allowed(self, agent_id: str, resource: str) -> bool:
        """Check if request is allowed within rate limits"""
        key = f"{agent_id}:{resource}"
        current_time = time.time()
        
        # Initialize tracking if needed
        if key not in self.request_counts:
            self.request_counts[key] = []
            self.time_windows[key] = current_time
            
        # Remove old requests outside time window
        window_size = self.config.get('rate_limit_window', 60)  # 1 minute default
        cutoff_time = current_time - window_size
        self.request_counts[key] = [
            req_time for req_time in self.request_counts[key] 
            if req_time > cutoff_time
        ]
        
        # Check against limit
        max_requests = self.config.get('max_requests_per_window', 100)
        if len(self.request_counts[key]) >= max_requests:
            return False
            
        # Record current request
        self.request_counts[key].append(current_time)
        return True

# Audit logging service
class AuditLogger:
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.log_destination = self._initialize_log_destination()
        
    def log_access_attempt(self, request: AccessRequest, authorized: bool):
        """Log access attempt for audit purposes"""
        audit_entry = {
            'timestamp': request.timestamp,
            'agent_id': request.agent_id,
            'resource': request.resource,
            'action': request.action,
            'authorized': authorized,
            'context': request.context
        }
        
        self._write_audit_log('access_attempt', audit_entry)
        
    def log_security_event(self, event_type: str, details: Dict[str, Any]):
        """Log security-related events"""
        audit_entry = {
            'timestamp': time.time(),
            'event_type': event_type,
            'details': details
        }
        
        self._write_audit_log('security_event', audit_entry)
        
    def _write_audit_log(self, category: str, entry: Dict[str, Any]):
        """Write audit entry to configured destination"""
        # In practice, use secure logging infrastructure
        log_entry = {
            'category': category,
            'timestamp': entry['timestamp'],
            'data': entry
        }
        
        print(f"AUDIT LOG [{category}]: {json.dumps(entry)}")

# Integration with agent lifecycle management
class SecureAgentLifecycleManager:
    def __init__(self, security_runtime: SecureAgentRuntime):
        self.security_runtime = security_runtime
        self.deployment_validator = self._initialize_deployment_validator()
        self.update_verifier = self._initialize_update_verifier()
        
    def initialize_secure_agent(self, agent_config: Dict[str, Any]) -> bool:
        """Initialize agent with security measures"""
        try:
            # Validate deployment environment
            if not self.deployment_validator.validate_environment():
                return False
                
            # Generate initial attestation
            initial_attestation = self.security_runtime.generate_attestation_report()
            self._store_initial_attestation(initial_attestation)
            
            # Initialize secure storage
            self._initialize_secure_storage()
            
            # Start security monitoring
            self._start_security_monitoring()
            
            return True
            
        except Exception as e:
            print(f"Agent initialization failed: {e}")
            return False
            
    def handle_agent_update(self, update_package: Dict[str, Any]) -> bool:
        """Handle secure agent updates"""
        try:
            # Verify update package integrity
            if not self.update_verifier.verify_update(update_package):
                return False
                
            # Generate pre-update attestation
            pre_update_attestation = self.security_runtime.generate_attestation_report()
            self._store_attestation(pre_update_attestation, 'pre_update')
            
            # Apply update securely
            update_result = self._apply_secure_update(update_package)
            
            if update_result.success:
                # Generate post-update attestation
                post_update_attestation = self.security_runtime.generate_attestation_report()
                self._store_attestation(post_update_attestation, 'post_update')
                
                # Notify monitoring systems
                self._notify_update_completion(update_package)
                
            return update_result.success
            
        except Exception as e:
            print(f"Update process failed: {e}")
            return False
            
    def perform_security_self_check(self) -> Dict[str, Any]:
        """Perform comprehensive security self-assessment"""
        check_results = {
            'timestamp': time.time(),
            'agent_id': self.security_runtime._get_current_agent_id(),
            'checks': {}
        }
        
        # Integrity verification
        check_results['checks']['integrity'] = self._verify_code_integrity()
        
        # Configuration validation
        check_results['checks']['configuration'] = self._validate_configuration()
        
        # Access control verification
        check_results['checks']['access_control'] = self._verify_access_controls()
        
        # Network security assessment
        check_results['checks']['network_security'] = self._assess_network_security()
        
        # Overall security posture
        check_results['overall_status'] = self._calculate_security_posture(check_results['checks'])
        
        return check_results
        
    def _initialize_deployment_validator(self):
        """Initialize deployment environment validator"""
        return DeploymentValidator()
        
    def _initialize_update_verifier(self):
        """Initialize secure update verifier"""
        return UpdateVerifier()
        
    def _store_initial_attestation(self, attestation: AttestationReport):
        """Store initial attestation report"""
        self._store_attestation(attestation, 'initial')
        
    def _store_attestation(self, attestation: AttestationReport, phase: str):
        """Store attestation report for audit trail"""
        attestation_data = {
            'phase': phase,
            'report': vars(attestation)
        }
        # Store in secure audit log or blockchain for immutability
        print(f"Stored {phase} attestation: {attestation.agent_id}")
        
    def _initialize_secure_storage(self):
        """Initialize secure data storage"""
        # Setup encrypted storage with proper key management
        pass
        
    def _start_security_monitoring(self):
        """Start security monitoring processes"""
        # Launch background security scanning and monitoring threads
        pass
        
    def _verify_code_integrity(self) -> Dict[str, Any]:
        """Verify agent code integrity"""
        return {
            'status': 'verified',
            'code_hash': self.security_runtime._get_agent_code_hash(),
            'verification_time': time.time()
        }
        
    def _validate_configuration(self) -> Dict[str, Any]:
        """Validate security configuration"""
        return {
            'status': 'valid',
            'policy': self.security_runtime.security_policy.value,
            'checks': ['encryption_enabled', 'access_controls_configured']
        }
        
    def _verify_access_controls(self) -> Dict[str, Any]:
        """Verify access control mechanisms"""
        return {
            'status': 'active',
            'controls': ['signature_verification', 'rate_limiting', 'identity_validation']
        }
        
    def _assess_network_security(self) -> Dict[str, Any]:
        """Assess network security posture"""
        import psutil
        
        connections = psutil.net_connections()
        secure_connections = [
            conn for conn in connections 
            if conn.status == 'ESTABLISHED' and self._is_connection_secure(conn)
        ]
        
        return {
            'status': 'monitored',
            'total_connections': len(connections),
            'secure_connections': len(secure_connections),
            'suspicious_activity': []
        }
        
    def _is_connection_secure(self, connection) -> bool:
        """Check if network connection is using secure protocols"""
        # Implementation would verify TLS/SSL encryption, certificate validity, etc.
        return True
        
    def _calculate_security_posture(self, checks: Dict[str, Any]) -> str:
        """Calculate overall security posture from individual checks"""
        # Simple aggregation - in practice, use weighted scoring
        failed_checks = [name for name, result in checks.items() 
                        if result.get('status') != 'verified' and result.get('status') != 'valid' and result.get('status') != 'active']
        
        if not failed_checks:
            return 'secure'
        elif len(failed_checks) <= 2:
            return 'degraded'
        else:
            return 'compromised'

# Update verification utilities
class UpdateVerifier:
    def verify_update(self, update_package: Dict[str, Any]) -> bool:
        """Verify update package integrity and authenticity"""
        # Check digital signature
        if not self._verify_signature(update_package):
            return False
            
        # Verify package integrity
        if not self._verify_integrity(update_package):
            return False
            
        # Validate update compatibility
        if not self._validate_compatibility(update_package):
            return False
            
        return True
        
    def _verify_signature(self, package: Dict[str, Any]) -> bool:
        """Verify update package digital signature"""
        # Implementation would use asymmetric cryptography to verify
        # that update came from trusted source
        return True  # Mock implementation
        
    def _verify_integrity(self, package: Dict[str, Any]) -> bool:
        """Verify update package integrity"""
        # Check hash values and file integrity
        return True  # Mock implementation
        
    def _validate_compatibility(self, package: Dict[str, Any]) -> bool:
        """Validate update compatibility with current system"""
        # Check version compatibility, dependency requirements, etc.
        return True  # Mock implementation

# Deployment environment validation
class DeploymentValidator:
    def validate_environment(self) -> bool:
        """Validate deployment environment meets security requirements"""
        validations = [
            self._check_container_security(),
            self._check_network_isolation(),
            self._check_file_system_permissions(),
            self._check_runtime_privileges()
        ]
        
        return all(validations)
        
    def _check_container_security(self) -> bool:
        """Check container security configurations"""
        # Verify running in container with proper security settings
        return True  # Mock implementation
        
    def _check_network_isolation(self) -> bool:
        """Check network isolation and firewall rules"""
        # Verify appropriate network restrictions are in place
        return True  # Mock implementation
        
    def _check_file_system_permissions(self) -> bool:
        """Check file system permission security"""
        # Verify appropriate file access controls
        return True  # Mock implementation
        
    def _check_runtime_privileges(self) -> bool:
        """Check runtime privilege levels"""
        # Verify not running with excessive privileges
        return True  # Mock implementation

# Example usage demonstrating secure agent operations
class SecureDemoAgent:
    def __init__(self, security_runtime: SecureAgentRuntime):
        self.security_runtime = security_runtime
        
    def process_sensitive_data(self, data: Dict[str, Any]) -> bool:
        """Process sensitive data with security measures"""
        # Request access to sensitive operations
        if not self.security_runtime.request_access('sensitive_data_processing', 'process', data):
            return False
            
        # Securely store intermediate results
        storage_key = f"processing_{hashlib.md5(str(data).encode()).hexdigest()}"
        if not self.security_runtime.secure_store_sensitive_data(storage_key, data):
            return False
            
        # Process data securely
        processed_data = self._perform_secure_processing(data)
        
        # Store results securely
        result_key = f"result_{storage_key}"
        return self.security_runtime.secure_store_sensitive_data(result_key, processed_data)
        
    def collaborate_with_peer(self, peer_id: str, message: Dict[str, Any]) -> bool:
        """Collaborate securely with peer agent"""
        # Request network access
        context = {'peer_id': peer_id, 'message_type': message.get('type')}
        if not self.security_runtime.request_access('network_communication', 'send', context):
            return False
            
        # Generate attestation for peer validation
        attestation = self.security_runtime.generate_attestation_report()
        
        # Send message with attestation (would be sent over secure channel)
        collaboration_request = {
            'message': message,
            'sender_attestation': vars(attestation),
            'timestamp': time.time()
        }
        
        print(f"Sending secure collaboration request to {peer_id}")
        return True
        
    def _perform_secure_processing(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """Perform data processing with security awareness"""
        # Mock secure processing logic
        return {
            'processed': True,
            'original_data_hash': hashlib.sha256(json.dumps(data).encode()).hexdigest(),
            'processing_timestamp': time.time(),
            'security_tags': ['processed', 'validated']
        }

# Demonstration of secure agent deployment and operation
def demonstrate_secure_deployment():
    """Demonstrate secure agent deployment with full security stack"""
    # Initialize security runtime
    config = {
        'security_policy': 'STRICT',
        'encryption_password': 'strong_password_123',
        'encryption_salt': base64.b64encode(os.urandom(16)).decode(),
        'agent_secret_key': 'agent_secret_key_456',
        'startup_time': time.time()
    }
    
    security_runtime = SecureAgentRuntime(config)
    
    # Initialize lifecycle manager
    lifecycle_manager = SecureAgentLifecycleManager(security_runtime)
    
    # Initialize secure agent
    if not lifecycle_manager.initialize_secure_agent(config):
        print("Failed to initialize secure agent")
        return
        
    # Perform security self-check
    self_check = lifecycle_manager.perform_security_self_check()
    print(f"Security self-check result: {self_check['overall_status']}")
    
    # Demonstrate secure operations with demo agent
    demo_agent = SecureDemoAgent(security_runtime)
    
    # Process sensitive data
    sensitive_data = {
        'user_id': 'user_123',
        'financial_records': [1000, 2500, 750],
        'personal_info': {'name': 'John Doe', 'ssn': '123-45-6789'}
    }
    
    if demo_agent.process_sensitive_data(sensitive_data):
        print("Sensitive data processed securely")
    else:
        print("Failed to process sensitive data")
        
    # Collaborate with peer agent
    peer_message = {
        'type': 'data_sharing_request',
        'data_subset': ['user_123', 'recent_activity'],
        'purpose': 'fraud_detection'
    }
    
    if demo_agent.collaborate_with_peer('fraud_detection_agent', peer_message):
        print("Secure collaboration initiated with peer agent")
    else:
        print("Failed to initiate secure collaboration")
        
    print("Secure deployment demonstration completed!")

# Run secure deployment demonstration
# demonstrate_secure_deployment()