Session 9: Production Agent Deployment - Test Solutions¶

# production/capstone_system.py
# Core system imports for production-ready multi-agent deployment
import asyncio
import logging
from typing import Dict, List, Any, Optional
from dataclasses import dataclass, field
from datetime import datetime
import yaml
import json

# Import our custom agent framework components
from workflows.advanced_engine import AdvancedWorkflowEngine
from a2a.router import MessageRouter
from a2a.registry import AgentRegistry
from agents.weather_agent import WeatherAgent
from agents.planning_agent import PlanningAgent
from monitoring.agent_metrics import AgentMetrics, HealthChecker

# Initialize structured logging for production monitoring
logger = logging.getLogger(__name__)

@dataclass
class SystemConfiguration:
    """Production system configuration with environment-specific settings."""
    environment: str = "production"
    log_level: str = "INFO"
    metrics_enabled: bool = True
    health_check_interval: int = 30
    max_agents: int = 100
    auto_scaling_enabled: bool = True
    security_enabled: bool = True

    # Kubernetes deployment configuration
    kubernetes_config: Dict[str, Any] = field(default_factory=lambda: {
        "namespace": "agent-system",
        "replicas": 3,
        "cpu_request": "500m",
        "memory_request": "512Mi",
        "cpu_limit": "2000m",
        "memory_limit": "2Gi"
    })

    # Monitoring and observability
    monitoring_config: Dict[str, Any] = field(default_factory=lambda: {
        "prometheus_enabled": True,
        "grafana_dashboards": True,
        "alert_manager": True,
        "log_aggregation": "elasticsearch"
    })

class ProductionAgentSystem:
    """Enterprise-grade production multi-agent system."""

    def __init__(self, config: SystemConfiguration):
        self.config = config
        self.workflow_engine = AdvancedWorkflowEngine()
        self.message_router = MessageRouter()
        self.agent_registry = AgentRegistry()
        self.metrics = AgentMetrics()
        self.health_checker = HealthChecker()
        self.agents: Dict[str, Any] = {}
        self.system_status = "initializing"

        # Initialize system components
        self._setup_logging()
        self._initialize_monitoring()

    def _setup_logging(self):
        """Configure structured logging for production monitoring."""
        logging.basicConfig(
            level=getattr(logging, self.config.log_level),
            format='{"timestamp": "%(asctime)s", "level": "%(levelname)s", "service": "agent-system", "message": "%(message)s"}',
            handlers=[
                logging.StreamHandler(),
                logging.FileHandler('/var/log/agent-system/system.log')
            ]
        )

    def _initialize_monitoring(self):
        """Initialize comprehensive monitoring and observability."""
        if self.config.monitoring_config["prometheus_enabled"]:
            self.metrics.initialize_prometheus_metrics()

        # Health check endpoints for Kubernetes liveness/readiness probes
        self.health_checker.register_health_check("system", self._system_health_check)
        self.health_checker.register_health_check("agents", self._agents_health_check)
        self.health_checker.register_health_check("workflows", self._workflows_health_check)

    async def initialize_system(self) -> bool:
        """Initialize the complete production agent system."""
        try:
            logger.info("Starting production agent system initialization")

            # Step 1: Initialize core components
            await self._initialize_core_components()

            # Step 2: Deploy standard agent fleet
            await self._deploy_agent_fleet()

            # Step 3: Setup workflows and routing
            await self._configure_workflows()

            # Step 4: Start monitoring and health checks
            await self._start_system_monitoring()

            # Step 5: Validate system readiness
            await self._validate_system_readiness()

            self.system_status = "operational"
            logger.info("Production agent system initialization completed successfully")

            return True

        except Exception as e:
            logger.error(f"System initialization failed: {str(e)}")
            self.system_status = "failed"
            raise

    async def _initialize_core_components(self):
        """Initialize workflow engine, message router, and registry."""
        # Initialize workflow engine with production settings
        await self.workflow_engine.initialize(
            max_concurrent_workflows=50,
            persistence_enabled=True,
            backup_enabled=True
        )

        # Initialize message router with load balancing
        await self.message_router.initialize(
            routing_strategy="load_balanced",
            retry_policy="exponential_backoff",
            dead_letter_queue=True
        )

        # Initialize agent registry with clustering support
        await self.agent_registry.initialize(
            clustering_enabled=True,
            replication_factor=3,
            consistency_level="strong"
        )

        logger.info("Core components initialized successfully")

    async def _deploy_agent_fleet(self):
        """Deploy the standard fleet of production agents."""

        # Deploy Weather Agent with high availability
        weather_agent = WeatherAgent(
            agent_id="weather_primary",
            config={
                "replicas": 2,
                "failover_enabled": True,
                "cache_enabled": True,
                "api_rate_limit": 1000
            }
        )
        await self._deploy_agent(weather_agent)

        # Deploy Planning Agent with load balancing
        planning_agent = PlanningAgent(
            agent_id="planning_primary",
            config={
                "replicas": 3,
                "load_balancing": True,
                "optimization_enabled": True,
                "batch_processing": True
            }
        )
        await self._deploy_agent(planning_agent)

        # Deploy Customer Service Agent
        customer_service_agent = CustomerServiceAgent(
            agent_id="customer_service",
            config={
                "replicas": 4,
                "priority_queues": True,
                "escalation_enabled": True,
                "sla_monitoring": True
            }
        )
        await self._deploy_agent(customer_service_agent)

        # Deploy Data Analysis Agent
        data_analysis_agent = DataAnalysisAgent(
            agent_id="data_analysis",
            config={
                "replicas": 2,
                "parallel_processing": True,
                "resource_intensive": True,
                "gpu_enabled": True
            }
        )
        await self._deploy_agent(data_analysis_agent)

        # Deploy Security Monitoring Agent
        security_agent = SecurityMonitoringAgent(
            agent_id="security_monitor",
            config={
                "replicas": 2,
                "real_time_monitoring": True,
                "threat_detection": True,
                "incident_response": True
            }
        )
        await self._deploy_agent(security_agent)

        logger.info(f"Deployed {len(self.agents)} agents in production fleet")

    async def _deploy_agent(self, agent: Any):
        """Deploy individual agent with production configuration."""
        try:
            # Register agent in the registry
            await self.agent_registry.register_agent(
                agent_id=agent.agent_id,
                capabilities=agent.get_capabilities(),
                metadata={
                    "deployment_time": datetime.now().isoformat(),
                    "version": agent.version,
                    "config": agent.config
                }
            )

            # Initialize agent with monitoring
            await agent.initialize()

            # Setup health monitoring
            self.health_checker.register_agent_health_check(
                agent.agent_id, 
                agent.health_check
            )

            # Add to active agents
            self.agents[agent.agent_id] = agent

            # Update metrics
            self.metrics.increment_counter("agents_deployed")

            logger.info(f"Agent {agent.agent_id} deployed successfully")

        except Exception as e:
            logger.error(f"Failed to deploy agent {agent.agent_id}: {str(e)}")
            raise

    async def _configure_workflows(self):
        """Configure production workflows with fault tolerance."""

        # Travel planning workflow with multiple agents
        travel_workflow = await self.workflow_engine.create_workflow(
            workflow_id="travel_planning_production",
            steps=[
                {
                    "id": "weather_check",
                    "agent_id": "weather_primary",
                    "action": "get_forecast",
                    "timeout": 30,
                    "retry_count": 3
                },
                {
                    "id": "route_planning",
                    "agent_id": "planning_primary",
                    "action": "plan_route",
                    "dependencies": ["weather_check"],
                    "timeout": 60,
                    "retry_count": 2
                },
                {
                    "id": "accommodation_search",
                    "agent_id": "planning_primary",
                    "action": "find_accommodation",
                    "dependencies": ["weather_check"],
                    "timeout": 45,
                    "parallel": True
                }
            ],
            fault_tolerance={
                "rollback_enabled": True,
                "compensation_actions": True,
                "circuit_breaker": True
            }
        )

        # Customer service workflow
        customer_service_workflow = await self.workflow_engine.create_workflow(
            workflow_id="customer_service_production",
            steps=[
                {
                    "id": "inquiry_classification",
                    "agent_id": "customer_service",
                    "action": "classify_inquiry",
                    "timeout": 15
                },
                {
                    "id": "issue_resolution",
                    "agent_id": "customer_service",
                    "action": "resolve_issue",
                    "dependencies": ["inquiry_classification"],
                    "timeout": 300
                },
                {
                    "id": "quality_assessment",
                    "agent_id": "data_analysis",
                    "action": "assess_resolution_quality",
                    "dependencies": ["issue_resolution"],
                    "timeout": 30
                }
            ],
            sla_requirements={
                "max_response_time": 600,  # 10 minutes
                "success_rate_threshold": 0.95
            }
        )

        logger.info("Production workflows configured successfully")

    async def _start_system_monitoring(self):
        """Start comprehensive system monitoring."""

        # Start health check monitoring
        await self.health_checker.start_monitoring(
            interval_seconds=self.config.health_check_interval
        )

        # Start metrics collection
        await self.metrics.start_collection()

        # Setup alerting rules
        await self._configure_alerting()

        logger.info("System monitoring started successfully")

    async def _configure_alerting(self):
        """Configure production alerting rules."""
        alerting_rules = [
            {
                "name": "agent_failure_rate_high",
                "condition": "agent_failure_rate > 0.05",
                "severity": "critical",
                "action": "restart_agent"
            },
            {
                "name": "workflow_latency_high", 
                "condition": "workflow_p95_latency > 300",
                "severity": "warning",
                "action": "scale_agents"
            },
            {
                "name": "system_memory_high",
                "condition": "system_memory_usage > 0.85",
                "severity": "warning",
                "action": "optimize_resources"
            },
            {
                "name": "security_threats_detected",
                "condition": "security_threats_count > 0",
                "severity": "critical",
                "action": "activate_security_protocol"
            }
        ]

        for rule in alerting_rules:
            await self.metrics.configure_alert(rule)

    async def _validate_system_readiness(self):
        """Validate that all system components are ready for production."""

        readiness_checks = [
            ("workflow_engine", self.workflow_engine.is_ready),
            ("message_router", self.message_router.is_ready),
            ("agent_registry", self.agent_registry.is_ready),
            ("health_checker", self.health_checker.is_ready),
            ("metrics_system", self.metrics.is_ready)
        ]

        failed_checks = []

        for check_name, check_func in readiness_checks:
            try:
                if await check_func():
                    logger.info(f"Readiness check passed: {check_name}")
                else:
                    failed_checks.append(check_name)
                    logger.error(f"Readiness check failed: {check_name}")
            except Exception as e:
                failed_checks.append(check_name)
                logger.error(f"Readiness check error for {check_name}: {str(e)}")

        if failed_checks:
            raise SystemError(f"System readiness validation failed for: {', '.join(failed_checks)}")

        logger.info("All system readiness checks passed")

    async def _system_health_check(self) -> Dict[str, Any]:
        """Comprehensive system health check."""
        return {
            "status": self.system_status,
            "uptime_seconds": (datetime.now() - self.start_time).total_seconds(),
            "active_agents": len(self.agents),
            "active_workflows": await self.workflow_engine.get_active_workflow_count(),
            "memory_usage_mb": self._get_memory_usage(),
            "cpu_usage_percent": self._get_cpu_usage()
        }

    async def _agents_health_check(self) -> Dict[str, Any]:
        """Health check for all deployed agents."""
        agent_statuses = {}

        for agent_id, agent in self.agents.items():
            try:
                health_status = await agent.health_check()
                agent_statuses[agent_id] = {
                    "status": "healthy" if health_status.get("healthy", False) else "unhealthy",
                    "last_activity": health_status.get("last_activity"),
                    "processed_requests": health_status.get("processed_requests", 0),
                    "error_rate": health_status.get("error_rate", 0.0)
                }
            except Exception as e:
                agent_statuses[agent_id] = {
                    "status": "error",
                    "error": str(e)
                }

        return agent_statuses

    async def _workflows_health_check(self) -> Dict[str, Any]:
        """Health check for workflow engine."""
        return {
            "active_workflows": await self.workflow_engine.get_active_workflow_count(),
            "completed_workflows_24h": await self.workflow_engine.get_completed_count(hours=24),
            "failed_workflows_24h": await self.workflow_engine.get_failed_count(hours=24),
            "average_execution_time": await self.workflow_engine.get_average_execution_time(),
            "success_rate": await self.workflow_engine.get_success_rate()
        }

    def _get_memory_usage(self) -> float:
        """Get current memory usage in MB."""
        import psutil
        process = psutil.Process()
        return process.memory_info().rss / 1024 / 1024

    def _get_cpu_usage(self) -> float:
        """Get current CPU usage percentage."""
        import psutil
        return psutil.cpu_percent(interval=1)

    async def execute_travel_planning_request(self, request_data: Dict[str, Any]) -> Dict[str, Any]:
        """Execute a travel planning request using the production workflow."""

        workflow_context = {
            "request_id": request_data.get("request_id", f"req_{int(datetime.now().timestamp())}"),
            "destination": request_data.get("destination"),
            "travel_dates": request_data.get("travel_dates"),
            "preferences": request_data.get("preferences", {}),
            "budget": request_data.get("budget"),
            "group_size": request_data.get("group_size", 1)
        }

        try:
            # Execute the workflow
            result = await self.workflow_engine.execute_workflow(
                workflow_id="travel_planning_production",
                context=workflow_context
            )

            # Update metrics
            self.metrics.increment_counter("travel_requests_completed")
            self.metrics.record_histogram("travel_planning_duration", result.get("execution_time", 0))

            return {
                "status": "success",
                "request_id": workflow_context["request_id"],
                "results": result,
                "execution_time": result.get("execution_time"),
                "quality_score": result.get("quality_score", 0.0)
            }

        except Exception as e:
            # Update error metrics
            self.metrics.increment_counter("travel_requests_failed")

            logger.error(f"Travel planning request failed: {str(e)}")

            return {
                "status": "failed",
                "request_id": workflow_context["request_id"],
                "error": str(e),
                "retry_recommended": True
            }

    async def handle_customer_service_inquiry(self, inquiry_data: Dict[str, Any]) -> Dict[str, Any]:
        """Handle customer service inquiry using the production workflow."""

        workflow_context = {
            "inquiry_id": inquiry_data.get("inquiry_id", f"inq_{int(datetime.now().timestamp())}"),
            "customer_id": inquiry_data.get("customer_id"),
            "subject": inquiry_data.get("subject"),
            "description": inquiry_data.get("description"),
            "priority": inquiry_data.get("priority", "normal"),
            "channel": inquiry_data.get("channel", "web")
        }

        try:
            # Execute customer service workflow
            result = await self.workflow_engine.execute_workflow(
                workflow_id="customer_service_production",
                context=workflow_context
            )

            # Update service metrics
            self.metrics.increment_counter("customer_inquiries_processed")
            self.metrics.record_histogram("customer_service_response_time", result.get("response_time", 0))

            return {
                "status": "resolved",
                "inquiry_id": workflow_context["inquiry_id"],
                "resolution": result.get("resolution"),
                "satisfaction_score": result.get("satisfaction_score"),
                "response_time": result.get("response_time"),
                "agent_involved": result.get("assigned_agent")
            }

        except Exception as e:
            self.metrics.increment_counter("customer_inquiries_failed")

            logger.error(f"Customer service inquiry failed: {str(e)}")

            return {
                "status": "failed",
                "inquiry_id": workflow_context["inquiry_id"],
                "error": str(e),
                "escalation_required": True
            }

    async def get_system_metrics(self) -> Dict[str, Any]:
        """Get comprehensive system metrics for monitoring dashboards."""

        return {
            "system_info": await self._system_health_check(),
            "agent_metrics": await self._agents_health_check(),
            "workflow_metrics": await self._workflows_health_check(),
            "performance_metrics": {
                "requests_per_minute": self.metrics.get_rate("total_requests"),
                "average_response_time": self.metrics.get_average("response_time"),
                "error_rate": self.metrics.get_rate("errors") / max(self.metrics.get_rate("total_requests"), 1),
                "throughput": self.metrics.get_counter("completed_workflows")
            },
            "resource_utilization": {
                "cpu_usage": self._get_cpu_usage(),
                "memory_usage_mb": self._get_memory_usage(),
                "active_connections": len(self.agents),
                "queue_depth": await self.message_router.get_queue_depth()
            }
        }

    async def shutdown_gracefully(self):
        """Perform graceful system shutdown."""
        logger.info("Initiating graceful system shutdown")

        self.system_status = "shutting_down"

        try:
            # Stop accepting new requests
            await self.message_router.stop_accepting_requests()

            # Complete active workflows
            await self.workflow_engine.complete_active_workflows(timeout=300)

            # Shutdown agents
            for agent_id, agent in self.agents.items():
                await agent.shutdown()
                logger.info(f"Agent {agent_id} shut down successfully")

            # Stop monitoring
            await self.health_checker.stop_monitoring()
            await self.metrics.stop_collection()

            # Final cleanup
            await self.workflow_engine.shutdown()
            await self.message_router.shutdown()
            await self.agent_registry.shutdown()

            self.system_status = "stopped"
            logger.info("System shutdown completed successfully")

        except Exception as e:
            logger.error(f"Error during system shutdown: {str(e)}")
            self.system_status = "shutdown_error"
            raise

# Production deployment example
async def deploy_production_system():
    """Deploy the complete production agent system."""

    # Load production configuration
    config = SystemConfiguration(
        environment="production",
        log_level="INFO",
        auto_scaling_enabled=True,
        kubernetes_config={
            "namespace": "agent-system-prod",
            "replicas": 5,
            "cpu_request": "1000m",
            "memory_request": "1Gi",
            "cpu_limit": "4000m",
            "memory_limit": "4Gi"
        }
    )

    # Initialize production system
    system = ProductionAgentSystem(config)

    try:
        # Initialize all components
        await system.initialize_system()

        print("🚀 Production agent system deployed successfully!")
        print(f"System status: {system.system_status}")
        print(f"Active agents: {len(system.agents)}")

        # Example usage
        print("\n📋 Testing travel planning workflow...")
        travel_result = await system.execute_travel_planning_request({
            "destination": "Tokyo, Japan",
            "travel_dates": {"start": "2024-03-15", "end": "2024-03-20"},
            "budget": 3000,
            "group_size": 2
        })
        print(f"Travel planning result: {travel_result['status']}")

        print("\n🎧 Testing customer service workflow...")
        service_result = await system.handle_customer_service_inquiry({
            "customer_id": "cust_12345",
            "subject": "Billing inquiry",
            "description": "Question about recent charges",
            "priority": "normal"
        })
        print(f"Customer service result: {service_result['status']}")

        print("\n📊 System metrics:")
        metrics = await system.get_system_metrics()
        print(f"System uptime: {metrics['system_info']['uptime_seconds']} seconds")
        print(f"Active workflows: {metrics['workflow_metrics']['active_workflows']}")
        print(f"CPU usage: {metrics['resource_utilization']['cpu_usage']:.2f}%")
        print(f"Memory usage: {metrics['resource_utilization']['memory_usage_mb']:.2f} MB")

        return system

    except Exception as e:
        print(f"❌ Production deployment failed: {str(e)}")
        raise

if __name__ == "__main__":
    # Run the production system deployment
    asyncio.run(deploy_production_system())

# production/capstone_system.py
# Core system imports for production-ready multi-agent deployment
import asyncio
import logging
from typing import Dict, List, Any, Optional
from dataclasses import dataclass, field
from datetime import datetime
import yaml
import json

# Import our custom agent framework components
from workflows.advanced_engine import AdvancedWorkflowEngine
from a2a.router import MessageRouter
from a2a.registry import AgentRegistry
from agents.weather_agent import WeatherAgent
from agents.planning_agent import PlanningAgent
from monitoring.agent_metrics import AgentMetrics, HealthChecker

# Initialize structured logging for production monitoring
logger = logging.getLogger(__name__)

@dataclass
class SystemConfiguration:
    """Production system configuration with enterprise defaults."""

    # Infrastructure settings - Kubernetes native
    kubernetes_namespace: str = "agent-system"
    redis_cluster_endpoint: str = "redis-cluster.agent-system.svc.cluster.local"
    prometheus_endpoint: str = "prometheus.monitoring.svc.cluster.local:9090"

    # Agent resource limits for optimal performance
    max_agents_per_node: int = 10
    agent_memory_limit: str = "1Gi"
    agent_cpu_limit: str = "1000m"

    # Monitoring and observability settings
    metrics_port: int = 9090
    health_check_interval: int = 30
    log_level: str = "INFO"

    # Security configuration for enterprise environments
    enable_mtls: bool = True
    jwt_secret_key: str = "production-secret-key"
    api_rate_limit: int = 1000

    # Performance tuning parameters
    workflow_timeout: int = 3600
    message_queue_size: int = 10000
    max_concurrent_workflows: int = 100