Production Best Practices

class ProductionReadinessCheck:
    def __init__(self):
        self.ants = AgenticAnts(api_key=os.getenv('AGENTICANTS_API_KEY'))
    
    def check_infrastructure_readiness(self):
        """Verify infrastructure is ready for production"""
        checks = []
        
        # Check API connectivity
        try:
            self.ants.health.check()
            checks.append(("API Connectivity", "✅ PASS"))
        except Exception as e:
            checks.append(("API Connectivity", f"❌ FAIL: {e}"))
        
        # Check monitoring setup
        try:
            metrics = self.ants.metrics.get_system_metrics()
            checks.append(("Monitoring Setup", "✅ PASS"))
        except Exception as e:
            checks.append(("Monitoring Setup", f"❌ FAIL: {e}"))
        
        # Check alerting configuration
        try:
            alerts = self.ants.alerts.list()
            checks.append(("Alerting Configuration", "✅ PASS"))
        except Exception as e:
            checks.append(("Alerting Configuration", f"❌ FAIL: {e}"))
        
        # Check backup systems
        try:
            backup_status = self.ants.backup.check_status()
            checks.append(("Backup Systems", "✅ PASS"))
        except Exception as e:
            checks.append(("Backup Systems", f"❌ FAIL: {e}"))
        
        return checks
    
    def print_readiness_report(self):
        """Print comprehensive readiness report"""
        print("🚀 Production Readiness Report")
        print("=" * 50)
        
        checks = self.check_infrastructure_readiness()
        
        for check_name, status in checks:
            print(f"{check_name}: {status}")
        
        # Overall status
        failed_checks = [check for check in checks if "❌" in check[1]]
        if failed_checks:
            print(f"\n❌ {len(failed_checks)} checks failed. Fix before deploying to production.")
        else:
            print("\n✅ All checks passed. Ready for production deployment!")

class ProductionSecurityCheck {
  private ants: AgenticAnts
  
  async checkSecurityReadiness() {
    const securityChecks = []
    
    // Check API key security
    try {
      await this.ants.security.validateApiKey()
      securityChecks.push({ check: 'API Key Security', status: 'PASS' })
    } catch (error) {
      securityChecks.push({ check: 'API Key Security', status: 'FAIL', error: error.message })
    }
    
    // Check data encryption
    try {
      const encryptionStatus = await this.ants.security.checkEncryption()
      securityChecks.push({ check: 'Data Encryption', status: 'PASS' })
    } catch (error) {
      securityChecks.push({ check: 'Data Encryption', status: 'FAIL', error: error.message })
    }
    
    // Check access controls
    try {
      const accessControls = await this.ants.security.checkAccessControls()
      securityChecks.push({ check: 'Access Controls', status: 'PASS' })
    } catch (error) {
      securityChecks.push({ check: 'Access Controls', status: 'FAIL', error: error.message })
    }
    
    // Check compliance requirements
    try {
      const complianceStatus = await this.ants.compliance.checkStatus()
      securityChecks.push({ check: 'Compliance Requirements', status: 'PASS' })
    } catch (error) {
      securityChecks.push({ check: 'Compliance Requirements', status: 'FAIL', error: error.message })
    }
    
    return securityChecks
  }
  
  async printSecurityReport() {
    console.log('🔒 Production Security Report')
    console.log('='.repeat(50))
    
    const checks = await this.checkSecurityReadiness()
    
    checks.forEach(check => {
      const status = check.status === 'PASS' ? '✅' : '❌'
      console.log(`${check.check}: ${status} ${check.status}`)
      if (check.error) {
        console.log(`  Error: ${check.error}`)
      }
    })
  }
}

class ProductionPerformanceBaseline:
    def __init__(self):
        self.ants = AgenticAnts(api_key=os.getenv('AGENTICANTS_API_KEY'))
    
    def establish_performance_baseline(self):
        """Establish performance baseline for production"""
        baseline_metrics = {}
        
        # Test response times
        response_times = []
        for i in range(100):
            start_time = time.time()
            # Simulate agent call
            result = self.simulate_agent_call()
            response_time = time.time() - start_time
            response_times.append(response_time)
        
        baseline_metrics["avg_response_time"] = sum(response_times) / len(response_times)
        baseline_metrics["p95_response_time"] = sorted(response_times)[95]
        baseline_metrics["p99_response_time"] = sorted(response_times)[99]
        
        # Test throughput
        throughput = self.test_throughput()
        baseline_metrics["max_throughput"] = throughput
        
        # Test error rates
        error_rate = self.test_error_rate()
        baseline_metrics["baseline_error_rate"] = error_rate
        
        return baseline_metrics
    
    def test_throughput(self):
        """Test maximum throughput"""
        # Implement throughput testing
        return 100  # requests per second
    
    def test_error_rate(self):
        """Test baseline error rate"""
        # Implement error rate testing
        return 0.01  # 1% error rate

class ProductionConfiguration:
    def __init__(self):
        self.ants = AgenticAnts(api_key=os.getenv('AGENTICANTS_API_KEY'))
    
    def configure_production_environment(self):
        """Configure AgenticAnts for production"""
        
        # Configure monitoring
        self.ants.config.set_production_mode(True)
        
        # Configure sampling
        self.ants.config.set_sampling_rate(1.0)  # 100% sampling in production
        
        # Configure retention
        self.ants.config.set_data_retention({
            "traces": "90_days",
            "metrics": "1_year",
            "logs": "7_years"
        })
        
        # Configure alerting
        self.ants.alerts.configure_production_alerts()
        
        # Configure backup
        self.ants.backup.configure_production_backup()
        
        print("✅ Production environment configured successfully")
    
    def configure_production_alerts(self):
        """Configure production-specific alerts"""
        
        # High error rate alert
        self.ants.alerts.create({
            "name": "High Error Rate",
            "condition": "error_rate > 5%",
            "window": "5m",
            "channels": ["email", "slack", "pagerduty"],
            "severity": "critical"
        })
        
        # High latency alert
        self.ants.alerts.create({
            "name": "High Latency",
            "condition": "p95_latency > 5000ms",
            "window": "10m",
            "channels": ["email", "slack"],
            "severity": "warning"
        })
        
        # Cost spike alert
        self.ants.alerts.create({
            "name": "Cost Spike",
            "condition": "daily_cost > 1000",
            "window": "1d",
            "channels": ["email", "slack"],
            "severity": "warning"
        })
        
        print("✅ Production alerts configured successfully")

class ProductionScaling {
  private ants: AgenticAnts
  
  async configureLoadBalancing() {
    // Configure load balancing for multiple instances
    await this.ants.config.setLoadBalancing({
      strategy: 'round_robin',
      healthCheckInterval: 30,
      maxRetries: 3,
      timeout: 5000
    })
    
    console.log('✅ Load balancing configured')
  }
  
  async configureAutoScaling() {
    // Configure auto-scaling based on metrics
    await this.ants.scaling.configureAutoScaling({
      minInstances: 2,
      maxInstances: 10,
      scaleUpThreshold: 80, // CPU usage
      scaleDownThreshold: 30,
      scaleUpCooldown: 300, // 5 minutes
      scaleDownCooldown: 600 // 10 minutes
    })
    
    console.log('✅ Auto-scaling configured')
  }
  
  async configureCircuitBreakers() {
    // Configure circuit breakers for resilience
    await this.ants.resilience.configureCircuitBreakers({
      failureThreshold: 5,
      recoveryTimeout: 30000,
      halfOpenMaxCalls: 3
    })
    
    console.log('✅ Circuit breakers configured')
  }
}

class ProductionDataManagement:
    def __init__(self):
        self.ants = AgenticAnts(api_key=os.getenv('AGENTICANTS_API_KEY'))
    
    def configure_data_management(self):
        """Configure data management for production"""
        
        # Configure data encryption
        self.ants.security.configure_encryption({
            "at_rest": "AES-256",
            "in_transit": "TLS-1.3",
            "key_rotation": "90_days"
        })
        
        # Configure data retention
        self.ants.data.configure_retention({
            "traces": "90_days",
            "metrics": "1_year",
            "logs": "7_years",
            "backups": "1_year"
        })
        
        # Configure data backup
        self.ants.backup.configure_backup({
            "frequency": "daily",
            "retention": "30_days",
            "encryption": True
        })
        
        # Configure data export
        self.ants.data.configure_export({
            "format": "json",
            "compression": True,
            "encryption": True
        })
        
        print("✅ Data management configured successfully")

class ProductionMonitoring:
    def __init__(self):
        self.ants = AgenticAnts(api_key=os.getenv('AGENTICANTS_API_KEY'))
    
    def setup_production_monitoring(self):
        """Setup comprehensive production monitoring"""
        
        # Setup real-time dashboards
        self.ants.dashboards.create_production_dashboard()
        
        # Setup health checks
        self.ants.health.setup_production_health_checks()
        
        # Setup performance monitoring
        self.ants.metrics.setup_production_metrics()
        
        # Setup cost monitoring
        self.ants.finops.setup_production_cost_monitoring()
        
        print("✅ Production monitoring setup complete")
    
    def create_production_dashboard(self):
        """Create production monitoring dashboard"""
        
        dashboard = self.ants.dashboards.create({
            "name": "Production Overview",
            "widgets": [
                {
                    "type": "timeseries",
                    "title": "Request Rate",
                    "metric": "requests_per_second",
                    "time_range": "1h"
                },
                {
                    "type": "timeseries",
                    "title": "Response Time",
                    "metric": "p95_latency",
                    "time_range": "1h"
                },
                {
                    "type": "gauge",
                    "title": "Error Rate",
                    "metric": "error_rate",
                    "thresholds": [5, 10]
                },
                {
                    "type": "timeseries",
                    "title": "Cost",
                    "metric": "daily_cost",
                    "time_range": "7d"
                }
            ]
        })
        
        return dashboard

class ProductionHealthChecks {
  private ants: AgenticAnts
  
  async setupHealthChecks() {
    // Configure health checks for all components
    await this.ants.health.configureHealthChecks({
      endpoints: [
        {
          name: 'api-health',
          url: '/api/health',
          interval: 30,
          timeout: 5,
          expectedStatus: 200
        },
        {
          name: 'database-health',
          url: '/api/db/health',
          interval: 60,
          timeout: 10,
          expectedStatus: 200
        },
        {
          name: 'vector-db-health',
          url: '/api/vector/health',
          interval: 60,
          timeout: 10,
          expectedStatus: 200
        }
      ]
    })
    
    console.log('✅ Health checks configured')
  }
  
  async getHealthStatus() {
    const healthStatus = await this.ants.health.getStatus()
    
    console.log('🏥 Production Health Status')
    console.log('='.repeat(50))
    
    Object.entries(healthStatus).forEach(([component, status]) => {
      const icon = status.healthy ? '✅' : '❌'
      console.log(`${component}: ${icon} ${status.status}`)
      if (!status.healthy) {
        console.log(`  Error: ${status.error}`)
      }
    })
    
    return healthStatus
  }
}

class ProductionPerformanceMonitoring:
    def __init__(self):
        self.ants = AgenticAnts(api_key=os.getenv('AGENTICANTS_API_KEY'))
    
    def monitor_production_performance(self):
        """Monitor production performance metrics"""
        
        # Get current performance metrics
        metrics = self.ants.metrics.get_production_metrics()
        
        print("📊 Production Performance Metrics")
        print("=" * 50)
        print(f"Request Rate: {metrics['request_rate']} req/s")
        print(f"Response Time (P95): {metrics['p95_latency']}ms")
        print(f"Error Rate: {metrics['error_rate']}%")
        print(f"Success Rate: {metrics['success_rate']}%")
        print(f"Active Connections: {metrics['active_connections']}")
        
        # Check for performance issues
        if metrics['error_rate'] > 5:
            print("⚠️  High error rate detected!")
        
        if metrics['p95_latency'] > 5000:
            print("⚠️  High latency detected!")
        
        if metrics['active_connections'] > 1000:
            print("⚠️  High connection count detected!")
        
        return metrics
    
    def get_performance_trends(self):
        """Get performance trends over time"""
        
        trends = self.ants.metrics.get_performance_trends(
            period="last_24h",
            granularity="1h"
        )
        
        print("📈 Performance Trends (Last 24h)")
        print("=" * 50)
        
        for trend in trends:
            print(f"{trend['timestamp']}: {trend['request_rate']} req/s, {trend['p95_latency']}ms")
        
        return trends

class ProductionIncidentResponse:
    def __init__(self):
        self.ants = AgenticAnts(api_key=os.getenv('AGENTICANTS_API_KEY'))
    
    def setup_incident_detection(self):
        """Setup incident detection and response"""
        
        # Configure incident detection rules
        self.ants.incidents.configure_detection_rules({
            "high_error_rate": {
                "condition": "error_rate > 10%",
                "window": "5m",
                "severity": "critical"
            },
            "high_latency": {
                "condition": "p95_latency > 10000ms",
                "window": "10m",
                "severity": "high"
            },
            "cost_spike": {
                "condition": "hourly_cost > 100",
                "window": "1h",
                "severity": "medium"
            }
        })
        
        # Configure incident response workflows
        self.ants.incidents.configure_response_workflows()
        
        print("✅ Incident detection and response configured")
    
    def handle_incident(self, incident_id: str):
        """Handle production incident"""
        
        incident = self.ants.incidents.get(incident_id)
        
        print(f"🚨 Incident {incident_id} detected")
        print(f"Severity: {incident['severity']}")
        print(f"Description: {incident['description']}")
        print(f"Affected systems: {incident['affected_systems']}")
        
        # Execute incident response workflow
        response = self.ants.incidents.execute_response_workflow(incident_id)
        
        print(f"Response actions: {response['actions']}")
        
        return response

class IncidentResponseWorkflows {
  private ants: AgenticAnts
  
  async configureResponseWorkflows() {
    // Configure automated response workflows
    await this.ants.incidents.configureWorkflows({
      critical: {
        steps: [
          { action: 'notify', channels: ['pagerduty', 'slack'] },
          { action: 'scale', instances: 2 },
          { action: 'enable', feature: 'circuit_breaker' },
          { action: 'notify', channels: ['slack'], message: 'Mitigation applied' }
        ]
      },
      high: {
        steps: [
          { action: 'notify', channels: ['slack'] },
          { action: 'investigate', automated: true },
          { action: 'notify', channels: ['slack'], message: 'Investigation started' }
        ]
      },
      medium: {
        steps: [
          { action: 'notify', channels: ['email'] },
          { action: 'log', level: 'warning' }
        ]
      }
    })
    
    console.log('✅ Incident response workflows configured')
  }
  
  async executeIncidentResponse(incidentId: string) {
    const incident = await this.ants.incidents.get(incidentId)
    
    console.log(`🚨 Executing incident response for ${incidentId}`)
    console.log(`Severity: ${incident.severity}`)
    
    // Execute appropriate workflow based on severity
    const workflow = await this.ants.incidents.getWorkflow(incident.severity)
    
    for (const step of workflow.steps) {
      console.log(`Executing: ${step.action}`)
      await this.ants.incidents.executeStep(step)
    }
    
    console.log('✅ Incident response workflow completed')
  }
}

class PostIncidentAnalysis:
    def __init__(self):
        self.ants = AgenticAnts(api_key=os.getenv('AGENTICANTS_API_KEY'))
    
    def conduct_post_incident_analysis(self, incident_id: str):
        """Conduct post-incident analysis"""
        
        incident = self.ants.incidents.get(incident_id)
        
        print(f"📋 Post-Incident Analysis for {incident_id}")
        print("=" * 50)
        
        # Analyze incident timeline
        timeline = self.ants.incidents.get_timeline(incident_id)
        print("Timeline:")
        for event in timeline:
            print(f"  {event['timestamp']}: {event['description']}")
        
        # Analyze root cause
        root_cause = self.ants.incidents.analyze_root_cause(incident_id)
        print(f"\nRoot Cause: {root_cause['description']}")
        
        # Generate recommendations
        recommendations = self.ants.incidents.generate_recommendations(incident_id)
        print("\nRecommendations:")
        for rec in recommendations:
            print(f"  - {rec['description']}")
        
        # Generate post-incident report
        report = self.ants.incidents.generate_post_incident_report(incident_id)
        
        return report

class ProductionScaling:
    def __init__(self):
        self.ants = AgenticAnts(api_key=os.getenv('AGENTICANTS_API_KEY'))
    
    def setup_horizontal_scaling(self):
        """Setup horizontal scaling for production"""
        
        # Configure auto-scaling
        self.ants.scaling.configure_auto_scaling({
            "min_instances": 2,
            "max_instances": 10,
            "scale_up_threshold": 80,  # CPU usage
            "scale_down_threshold": 30,
            "scale_up_cooldown": 300,  # 5 minutes
            "scale_down_cooldown": 600  # 10 minutes
        })
        
        # Configure load balancing
        self.ants.scaling.configure_load_balancing({
            "strategy": "round_robin",
            "health_check_interval": 30,
            "max_retries": 3,
            "timeout": 5000
        })
        
        print("✅ Horizontal scaling configured")
    
    def monitor_scaling_metrics(self):
        """Monitor scaling metrics"""
        
        metrics = self.ants.scaling.get_scaling_metrics()
        
        print("📊 Scaling Metrics")
        print("=" * 50)
        print(f"Current Instances: {metrics['current_instances']}")
        print(f"CPU Usage: {metrics['cpu_usage']}%")
        print(f"Memory Usage: {metrics['memory_usage']}%")
        print(f"Request Rate: {metrics['request_rate']} req/s")
        
        # Check scaling triggers
        if metrics['cpu_usage'] > 80:
            print("⚠️  High CPU usage - scaling up may be triggered")
        
        if metrics['cpu_usage'] < 30:
            print("ℹ️  Low CPU usage - scaling down may be triggered")
        
        return metrics

class VerticalScaling {
  private ants: AgenticAnts
  
  async setupVerticalScaling() {
    // Configure vertical scaling based on resource usage
    await this.ants.scaling.configureVerticalScaling({
      cpuThreshold: 80,
      memoryThreshold: 85,
      scaleUpFactor: 1.5,
      scaleDownFactor: 0.8,
      cooldownPeriod: 300
    })
    
    console.log('✅ Vertical scaling configured')
  }
  
  async monitorResourceUsage() {
    const resourceMetrics = await this.ants.scaling.getResourceMetrics()
    
    console.log('📊 Resource Usage Metrics')
    console.log('='.repeat(50))
    console.log(`CPU Usage: ${resourceMetrics.cpuUsage}%`)
    console.log(`Memory Usage: ${resourceMetrics.memoryUsage}%`)
    console.log(`Disk Usage: ${resourceMetrics.diskUsage}%`)
    console.log(`Network Usage: ${resourceMetrics.networkUsage}%`)
    
    // Check for resource constraints
    if (resourceMetrics.cpuUsage > 90) {
      console.log('⚠️  High CPU usage detected')
    }
    
    if (resourceMetrics.memoryUsage > 90) {
      console.log('⚠️  High memory usage detected')
    }
    
    return resourceMetrics
  }
}

class ProductionReadyAgent:
    def __init__(self):
        self.ants = AgenticAnts(api_key=os.getenv('AGENTICANTS_API_KEY'))
    
    def process_request(self, request: dict):
        """Process request with production-ready error handling"""
        
        # Create trace for monitoring
        trace = self.ants.trace.create(
            name="production-agent",
            input=request,
            metadata={
                "environment": "production",
                "version": "1.0.0"
            }
        )
        
        try:
            # Validate request
            self.validate_request(request)
            
            # Process request
            result = self.process_core_logic(request)
            
            # Log success
            trace.complete(
                output=result,
                metadata={
                    "success": True,
                    "processing_time": trace.duration
                }
            )
            
            return result
            
        except ValidationError as e:
            trace.error(error=str(e), metadata={"error_type": "validation"})
            raise HTTPException(status_code=400, detail=str(e))
        
        except ProcessingError as e:
            trace.error(error=str(e), metadata={"error_type": "processing"})
            raise HTTPException(status_code=500, detail="Internal processing error")
        
        except Exception as e:
            trace.error(error=str(e), metadata={"error_type": "unexpected"})
            raise HTTPException(status_code=500, detail="Internal server error")
    
    def validate_request(self, request: dict):
        """Validate incoming request"""
        if not request.get("input"):
            raise ValidationError("Input is required")
        
        if len(request["input"]) > 10000:
            raise ValidationError("Input too long")
    
    def process_core_logic(self, request: dict):
        """Core business logic"""
        # Implement your agent logic here
        return {"result": "processed"}

class ProductionMonitoring {
  private ants: AgenticAnts
  
  async setupProductionMonitoring() {
    // Configure comprehensive monitoring
    await this.ants.monitoring.configure({
      metrics: {
        performance: true,
        errors: true,
        costs: true,
        security: true
      },
      alerting: {
        enabled: true,
        channels: ['email', 'slack', 'pagerduty']
      },
      dashboards: {
        realTime: true,
        historical: true
      }
    })
    
    console.log('✅ Production monitoring configured')
  }
  
  async createProductionDashboards() {
    // Create production dashboards
    const dashboards = [
      {
        name: 'Production Overview',
        widgets: [
          { type: 'timeseries', metric: 'request_rate' },
          { type: 'gauge', metric: 'error_rate' },
          { type: 'timeseries', metric: 'response_time' }
        ]
      },
      {
        name: 'Cost Monitoring',
        widgets: [
          { type: 'timeseries', metric: 'daily_cost' },
          { type: 'counter', metric: 'total_cost' }
        ]
      }
    ]
    
    for (const dashboard of dashboards) {
      await this.ants.dashboards.create(dashboard)
    }
    
    console.log('✅ Production dashboards created')
  }
}

def debug_production_errors():
    # Analyze production errors
    errors = ants.traces.query({
        "status": "error",
        "environment": "production",
        "period": "last_24h"
    })
    
    print("Production Error Analysis:")
    for error in errors:
        print(f"Error: {error.error}")
        print(f"Time: {error.timestamp}")
        print(f"Agent: {error.metadata.get('agent')}")
        print(f"Request: {error.input}")
        print("---")

async function debugPerformanceDegradation() {
  const performanceIssues = await ants.metrics.getPerformanceIssues({
    period: 'last_24h',
    threshold: 5000 // 5 seconds
  })
  
  console.log('Performance Issues:')
  performanceIssues.forEach(issue => {
    console.log(`Agent: ${issue.agent}`)
    console.log(`Latency: ${issue.latency}ms`)
    console.log(`Time: ${issue.timestamp}`)
  })
}