⚙️ Session 6: Production Deployment¶

import os
import json
from typing import Dict, List, Any, Optional
from dataclasses import dataclass

@dataclass
class ProductionConfig:
    """Production configuration for atomic agent systems"""
    environment: str
    max_agents: int
    memory_limit: int
    token_limit: int
    monitoring_enabled: bool
    log_level: str
    security_config: Dict

class ProductionSystemBootstrap:
    """Bootstrap atomic agent systems for production deployment"""

    def __init__(self, config_path: str):
        self.config = self._load_config(config_path)
        self.system_components = {}
        self.monitoring_agents = {}
        self.health_status = {"status": "initializing", "components": {}}

    def _load_config(self, config_path: str) -> ProductionConfig:
        """Load production configuration from file"""
        try:
            with open(config_path, 'r') as f:
                config_data = json.load(f)

            return ProductionConfig(
                environment=config_data.get("environment", "production"),
                max_agents=config_data.get("max_agents", 100),
                memory_limit=config_data.get("memory_limit", 1000),
                token_limit=config_data.get("token_limit", 500),
                monitoring_enabled=config_data.get("monitoring_enabled", True),
                log_level=config_data.get("log_level", "INFO"),
                security_config=config_data.get("security", {})
            )
        except Exception as e:
            # Fallback to default configuration
            return ProductionConfig(
                environment="production",
                max_agents=50,
                memory_limit=500,
                token_limit=400,
                monitoring_enabled=True,
                log_level="INFO",
                security_config={}
            )

    def initialize_production_system(self) -> Dict:
        """Initialize complete production system"""

        initialization_steps = [
            ("core_orchestrator", self._setup_core_orchestrator),
            ("monitoring_system", self._setup_monitoring),
            ("security_layer", self._setup_security),
            ("health_checks", self._setup_health_checks)
        ]

        initialization_results = {}

        for step_name, setup_function in initialization_steps:
            try:
                result = setup_function()
                initialization_results[step_name] = {"status": "success", "details": result}
                self.health_status["components"][step_name] = "operational"
            except Exception as e:
                initialization_results[step_name] = {"status": "failed", "error": str(e)}
                self.health_status["components"][step_name] = "failed"

        self.health_status["status"] = "operational" if all(
            status == "operational" for status in self.health_status["components"].values()
        ) else "degraded"

        return {
            "system_status": self.health_status["status"],
            "initialization_results": initialization_results,
            "configuration": {
                "environment": self.config.environment,
                "max_agents": self.config.max_agents,
                "monitoring_enabled": self.config.monitoring_enabled
            }
        }

class ProductionScalingManager:
    """Manage scaling of atomic agent systems in production"""

    def __init__(self, bootstrap_system: ProductionSystemBootstrap):
        self.bootstrap = bootstrap_system
        self.active_agents = {}
        self.scaling_metrics = {
            "current_load": 0,
            "average_response_time": 0,
            "agent_utilization": 0,
            "scaling_events": []
        }
        self.scaling_thresholds = {
            "scale_up_cpu": 70,
            "scale_up_memory": 80,
            "scale_down_cpu": 30,
            "scale_down_memory": 40
        }

    def evaluate_scaling_needs(self, current_metrics: Dict) -> Dict:
        """Evaluate if scaling is needed based on current metrics"""

        recommendations = []

        # CPU-based scaling
        cpu_usage = current_metrics.get("cpu_usage", 0)
        if cpu_usage > self.scaling_thresholds["scale_up_cpu"]:
            recommendations.append({
                "type": "scale_up",
                "reason": "high_cpu_usage",
                "metric": cpu_usage,
                "recommended_action": "add_agents"
            })
        elif cpu_usage < self.scaling_thresholds["scale_down_cpu"]:
            recommendations.append({
                "type": "scale_down",
                "reason": "low_cpu_usage",
                "metric": cpu_usage,
                "recommended_action": "remove_agents"
            })

        # Memory-based scaling
        memory_usage = current_metrics.get("memory_usage", 0)
        if memory_usage > self.scaling_thresholds["scale_up_memory"]:
            recommendations.append({
                "type": "scale_up",
                "reason": "high_memory_usage",
                "metric": memory_usage,
                "recommended_action": "add_memory_optimized_agents"
            })

        return {
            "scaling_needed": len(recommendations) > 0,
            "recommendations": recommendations,
            "current_metrics": current_metrics,
            "timestamp": "current_time"
        }

    def execute_scaling_action(self, action: Dict) -> Dict:
        """Execute scaling action based on evaluation"""

        action_type = action.get("type")
        reason = action.get("reason")

        try:
            if action_type == "scale_up":
                return self._scale_up_agents(reason)
            elif action_type == "scale_down":
                return self._scale_down_agents(reason)
            else:
                return {"status": "no_action", "reason": "unknown_action_type"}

        except Exception as e:
            return {"status": "failed", "error": str(e)}

    def _scale_up_agents(self, reason: str) -> Dict:
        """Scale up atomic agents based on demand"""

        current_count = len(self.active_agents)
        max_agents = self.bootstrap.config.max_agents

        if current_count >= max_agents:
            return {
                "status": "limit_reached",
                "message": f"Cannot scale beyond {max_agents} agents"
            }

        # Create new agent instance
        new_agent_id = f"agent_{current_count + 1}"
        new_agent = BaseAgent(
            agent_name=new_agent_id,
            system_prompt="Production data processing agent",
            memory=None,  # Stateless for scalability
            max_tokens=self.bootstrap.config.token_limit
        )

        self.active_agents[new_agent_id] = {
            "agent": new_agent,
            "created_at": "current_time",
            "reason": reason,
            "status": "active"
        }

        # Record scaling event
        self.scaling_metrics["scaling_events"].append({
            "action": "scale_up",
            "agent_id": new_agent_id,
            "reason": reason,
            "timestamp": "current_time"
        })

        return {
            "status": "success",
            "action": "scale_up",
            "new_agent_id": new_agent_id,
            "total_agents": len(self.active_agents)
        }

class ProductionMonitoringSystem:
    """Complete monitoring system for production atomic agents"""

    def __init__(self):
        self.metrics_store = {}
        self.alert_rules = {}
        self.monitoring_agents = {}

    def setup_monitoring_agents(self) -> Dict:
        """Initialize monitoring agents for system observability"""

        # Performance monitoring agent
        self.monitoring_agents["performance"] = BaseAgent(
            agent_name="performance_monitor",
            system_prompt="Monitor system performance metrics and identify bottlenecks",
            memory=ChatMemory(max_messages=50),
            max_tokens=300
        )

        # Error monitoring agent
        self.monitoring_agents["error_tracking"] = BaseAgent(
            agent_name="error_tracker",
            system_prompt="Track and analyze system errors for root cause analysis",
            memory=ChatMemory(max_messages=100),
            max_tokens=400
        )

        # Security monitoring agent
        self.monitoring_agents["security"] = BaseAgent(
            agent_name="security_monitor",
            system_prompt="Monitor security events and detect anomalous behavior",
            memory=ChatMemory(max_messages=200),
            max_tokens=250
        )

        return {
            "monitoring_agents_initialized": len(self.monitoring_agents),
            "agents": list(self.monitoring_agents.keys())
        }

    def collect_system_metrics(self, system_state: Dict) -> Dict:
        """Collect comprehensive system metrics"""

        current_metrics = {
            "timestamp": "current_time",
            "agent_count": system_state.get("active_agents", 0),
            "processing_queue_size": system_state.get("queue_size", 0),
            "average_response_time": system_state.get("avg_response_time", 0),
            "error_rate": system_state.get("error_rate", 0),
            "memory_usage": system_state.get("memory_usage", 0),
            "cpu_usage": system_state.get("cpu_usage", 0)
        }

        # Store metrics for trending
        timestamp = current_metrics["timestamp"]
        self.metrics_store[timestamp] = current_metrics

        # Analyze metrics with monitoring agents
        analysis_results = {}

        for monitor_type, monitor_agent in self.monitoring_agents.items():
            try:
                analysis_prompt = f"Analyze {monitor_type} metrics: {str(current_metrics)}"
                analysis = monitor_agent.run(analysis_prompt)
                analysis_results[monitor_type] = analysis
            except Exception as e:
                analysis_results[monitor_type] = f"Monitoring error: {str(e)}"

        return {
            "metrics": current_metrics,
            "analysis": analysis_results,
            "metric_history_size": len(self.metrics_store)
        }

    def check_alert_conditions(self, metrics: Dict) -> List[Dict]:
        """Check metrics against alert conditions"""

        alerts = []

        # Error rate alerts
        error_rate = metrics.get("error_rate", 0)
        if error_rate > 5:  # 5% error rate threshold
            alerts.append({
                "type": "error_rate_high",
                "severity": "critical" if error_rate > 10 else "warning",
                "value": error_rate,
                "threshold": 5,
                "message": f"Error rate {error_rate}% exceeds threshold"
            })

        # Response time alerts
        response_time = metrics.get("average_response_time", 0)
        if response_time > 5000:  # 5 second threshold
            alerts.append({
                "type": "response_time_high",
                "severity": "warning",
                "value": response_time,
                "threshold": 5000,
                "message": f"Response time {response_time}ms exceeds threshold"
            })

        return alerts

class ProductionSecurityManager:
    """Security and compliance management for production atomic agents"""

    def __init__(self, config: ProductionConfig):
        self.config = config
        self.security_policies = {}
        self.audit_log = []
        self.access_control = {}

    def setup_security_framework(self) -> Dict:
        """Initialize production security framework"""

        security_components = []

        # Setup access control
        access_result = self._setup_access_control()
        security_components.append({"component": "access_control", "result": access_result})

        # Setup audit logging
        audit_result = self._setup_audit_logging()
        security_components.append({"component": "audit_logging", "result": audit_result})

        # Setup data encryption
        encryption_result = self._setup_data_encryption()
        security_components.append({"component": "data_encryption", "result": encryption_result})

        return {
            "security_framework_status": "initialized",
            "components": security_components,
            "compliance_level": "enterprise"
        }

    def _setup_access_control(self) -> Dict:
        """Setup access control for atomic agents"""

        # Define access levels
        self.access_control = {
            "admin": ["create_agent", "delete_agent", "modify_config", "view_all"],
            "operator": ["view_metrics", "restart_agent", "view_logs"],
            "read_only": ["view_metrics", "view_public_logs"]
        }

        return {
            "access_levels_defined": len(self.access_control),
            "permissions": self.access_control
        }

    def _setup_audit_logging(self) -> Dict:
        """Setup comprehensive audit logging"""

        audit_categories = [
            "agent_lifecycle",
            "configuration_changes",
            "security_events",
            "performance_events",
            "error_events"
        ]

        return {
            "audit_categories": audit_categories,
            "logging_enabled": True
        }

    def log_security_event(self, event_type: str, details: Dict) -> None:
        """Log security event for audit trail"""

        event_entry = {
            "timestamp": "current_time",
            "event_type": event_type,
            "details": details,
            "user_id": details.get("user_id", "system"),
            "ip_address": details.get("ip_address", "localhost"),
            "severity": details.get("severity", "info")
        }

        self.audit_log.append(event_entry)

        # In production, this would write to secure log storage
        print(f"Security Event Logged: {event_type}")

    def validate_compliance(self) -> Dict:
        """Validate system compliance with security policies"""

        compliance_checks = [
            ("access_control", self._check_access_control_compliance),
            ("audit_logging", self._check_audit_compliance),
            ("data_encryption", self._check_encryption_compliance)
        ]

        compliance_results = {}
        overall_compliance = True

        for check_name, check_function in compliance_checks:
            try:
                result = check_function()
                compliance_results[check_name] = result
                if not result.get("compliant", False):
                    overall_compliance = False
            except Exception as e:
                compliance_results[check_name] = {"compliant": False, "error": str(e)}
                overall_compliance = False

        return {
            "overall_compliance": overall_compliance,
            "detailed_results": compliance_results,
            "compliance_level": "full" if overall_compliance else "partial"
        }

class CompleteProductionSystem:
    """Complete production deployment system for atomic agents"""

    def __init__(self, config_path: str):
        self.bootstrap = ProductionSystemBootstrap(config_path)
        self.scaling_manager = ProductionScalingManager(self.bootstrap)
        self.monitoring = ProductionMonitoringSystem()
        self.security = ProductionSecurityManager(self.bootstrap.config)
        self.deployment_status = {"status": "initializing"}

    def deploy_to_production(self) -> Dict:
        """Complete production deployment workflow"""

        deployment_steps = [
            ("system_initialization", self._initialize_system),
            ("security_setup", self._setup_security),
            ("monitoring_activation", self._activate_monitoring),
            ("health_verification", self._verify_system_health),
            ("production_readiness", self._check_production_readiness)
        ]

        deployment_results = {}

        for step_name, step_function in deployment_steps:
            try:
                step_result = step_function()
                deployment_results[step_name] = {
                    "status": "success",
                    "result": step_result
                }
            except Exception as e:
                deployment_results[step_name] = {
                    "status": "failed",
                    "error": str(e)
                }
                # Stop deployment on critical failures
                break

        # Determine overall deployment status
        success_count = sum(
            1 for result in deployment_results.values()
            if result.get("status") == "success"
        )

        self.deployment_status = {
            "status": "success" if success_count == len(deployment_steps) else "partial",
            "successful_steps": success_count,
            "total_steps": len(deployment_steps),
            "deployment_results": deployment_results
        }

        return self.deployment_status

    def _initialize_system(self) -> Dict:
        """Initialize the complete system"""
        return self.bootstrap.initialize_production_system()

    def _setup_security(self) -> Dict:
        """Setup security framework"""
        return self.security.setup_security_framework()

    def _activate_monitoring(self) -> Dict:
        """Activate monitoring system"""
        return self.monitoring.setup_monitoring_agents()

    def _verify_system_health(self) -> Dict:
        """Verify system health before production"""
        # Collect initial metrics
        test_metrics = {
            "active_agents": 1,
            "queue_size": 0,
            "avg_response_time": 100,
            "error_rate": 0,
            "memory_usage": 25,
            "cpu_usage": 15
        }

        monitoring_result = self.monitoring.collect_system_metrics(test_metrics)
        alerts = self.monitoring.check_alert_conditions(test_metrics)

        return {
            "health_status": "healthy" if len(alerts) == 0 else "warning",
            "monitoring_result": monitoring_result,
            "alerts": alerts
        }

    def _check_production_readiness(self) -> Dict:
        """Final production readiness check"""
        compliance_result = self.security.validate_compliance()

        return {
            "production_ready": compliance_result.get("overall_compliance", False),
            "compliance_status": compliance_result.get("compliance_level", "unknown"),
            "readiness_timestamp": "current_time"
        }

class BlueGreenDeployment:
    """Blue-green deployment for atomic agent systems"""

    def __init__(self):
        self.blue_environment = {}
        self.green_environment = {}
        self.active_environment = "blue"

    def deploy_to_inactive(self, new_system_config: Dict) -> Dict:
        """Deploy new version to inactive environment"""
        inactive_env = "green" if self.active_environment == "blue" else "blue"

        # Deploy to inactive environment
        deployment_result = self._deploy_environment(inactive_env, new_system_config)

        return {
            "deployment_environment": inactive_env,
            "deployment_result": deployment_result,
            "ready_for_switch": deployment_result.get("status") == "success"
        }

    def switch_traffic(self) -> Dict:
        """Switch traffic to new deployment"""
        new_active = "green" if self.active_environment == "blue" else "blue"
        old_active = self.active_environment

        self.active_environment = new_active

        return {
            "traffic_switched": True,
            "old_environment": old_active,
            "new_environment": new_active,
            "switch_timestamp": "current_time"
        }

class CanaryDeployment:
    """Canary deployment for atomic agent systems"""

    def __init__(self):
        self.production_agents = {}
        self.canary_agents = {}
        self.traffic_split = {"production": 100, "canary": 0}

    def deploy_canary(self, canary_config: Dict, traffic_percentage: int = 5) -> Dict:
        """Deploy canary version with limited traffic"""

        # Create canary agents
        canary_count = max(1, len(self.production_agents) * traffic_percentage // 100)

        for i in range(canary_count):
            agent_id = f"canary_agent_{i}"
            self.canary_agents[agent_id] = self._create_canary_agent(canary_config)

        # Update traffic split
        self.traffic_split = {
            "production": 100 - traffic_percentage,
            "canary": traffic_percentage
        }

        return {
            "canary_agents_deployed": canary_count,
            "traffic_split": self.traffic_split,
            "canary_status": "monitoring"
        }

def test_production_deployment():
    """Test complete production deployment system"""

    # Test system initialization
    config_path = "test_production_config.json"
    production_system = CompleteProductionSystem(config_path)

    # Test deployment workflow
    deployment_result = production_system.deploy_to_production()

    assert deployment_result["status"] in ["success", "partial"]
    assert deployment_result["successful_steps"] > 0

    # Test scaling functionality
    test_metrics = {"cpu_usage": 80, "memory_usage": 70}
    scaling_evaluation = production_system.scaling_manager.evaluate_scaling_needs(test_metrics)

    assert "recommendations" in scaling_evaluation

    # Test monitoring system
    monitoring_result = production_system.monitoring.collect_system_metrics({
        "active_agents": 5,
        "error_rate": 2,
        "avg_response_time": 200
    })

    assert "metrics" in monitoring_result
    assert "analysis" in monitoring_result

    print("✅ Production deployment tests passed!")