Production Deployment

Production deployment guide for BoxLang AI - monitoring, error handling, performance optimization, and best practices.

Comprehensive guide for deploying BoxLang AI applications to production environments. Learn about monitoring, error handling, performance optimization, security, and operational best practices.

📋 Table of Contents

✅ Pre-Deployment Checklist

Essential Requirements

Before deploying to production, ensure you have:

✅ API Keys Secured - Stored in environment variables or secrets manager
✅ Error Handling - Comprehensive try-catch blocks around AI calls
✅ Rate Limiting - Client-side request throttling implemented
✅ Monitoring - Logging and alerting configured
✅ Fallback Strategy - Secondary provider or graceful degradation
✅ Timeout Configuration - Appropriate timeouts for your use case
✅ Cost Limits - Budget alerts and usage tracking
✅ Health Checks - Endpoints to verify service availability
✅ Load Testing - Performance validated under expected load
✅ Backup Provider - Alternative AI provider configured

Configuration Validation

// Validate configuration on startup
function validateAIConfiguration() {
    required = [
        "OPENAI_API_KEY",
        "CLAUDE_API_KEY",  // Backup provider
        "AI_TIMEOUT_SECONDS",
        "AI_MAX_RETRIES"
    ]

    missing = []
    for ( key in required ) {
        if ( isNull( getSystemSetting( key, "" ) ) || getSystemSetting( key ) == "" ) {
            missing.append( key )
        }
    }

    if ( !missing.isEmpty() ) {
        throw "Missing required AI configuration: #missing.toList()#"
    }

    writeLog( "AI configuration validated successfully" )
}

// Run on application start
BoxAnnounce( "onApplicationStart", {
    listener: validateAIConfiguration
} )

⚙️ Configuration Management

Environment-Based Configuration

Separate configs per environment:

// config/ai-config.bx
class {
    function getAIConfig() {
        var env = getSystemSetting( "ENVIRONMENT", "production" )

        var configs = {
            "development": {
                provider: "ollama",           // Free for dev
                model: "llama3.2:3b",
                timeout: 30,
                retries: 1,
                logLevel: "DEBUG"
            },
            "staging": {
                provider: "openai",
                model: "gpt-3.5-turbo",       // Cheaper for staging
                timeout: 15,
                retries: 2,
                logLevel: "INFO"
            },
            "production": {
                provider: "openai",
                model: "gpt-4o",              // Best quality for prod
                fallbackProvider: "claude",
                timeout: 10,
                retries: 3,
                logLevel: "WARN",
                enableCaching: true,
                enableMonitoring: true
            }
        }

        return configs[ env ]
    }
}

Secrets Management

Never hardcode API keys:

// ❌ WRONG
apiKey = "sk-1234567890abcdef"

// ✅ RIGHT - Environment variables
apiKey = getSystemSetting( "OPENAI_API_KEY" )

// ✅ RIGHT - AWS Secrets Manager
function getAPIKey( secretName ) {
    return awsSecretsManager.getSecret( secretName ).getValue()
}

// ✅ RIGHT - Azure Key Vault
function getAPIKey( secretName ) {
    return azureKeyVault.getSecret( secretName )
}

// ✅ RIGHT - HashiCorp Vault
function getAPIKey( path ) {
    return vaultClient.read( path ).data.apiKey
}

Dynamic Configuration Reloading

// Reload config without restarting app
class singleton {
    property name="config" type="struct";
    property name="lastReload" type="date";

    function init() {
        reloadConfig()
        return this
    }

    function reloadConfig() {
        variables.config = deserializeJSON(
            fileRead( "/config/ai-production.json" )
        )
        variables.lastReload = now()
        writeLog( "AI configuration reloaded" )
    }

    function getConfig() {
        // Auto-reload every 5 minutes
        if ( dateDiff( "n", variables.lastReload, now() ) > 5 ) {
            reloadConfig()
        }
        return variables.config
    }
}

🛡️ Error Handling & Resilience

Comprehensive Error Handling

class {
    function safeAIChat( required string prompt, struct params = {}, struct options = {} ) {
        var maxRetries = params.maxRetries ?: 3
        var retryDelay = params.retryDelay ?: 1000  // milliseconds

        for ( var attempt = 1; attempt <= maxRetries; attempt++ ) {
            try {
                return aiChat(
                    arguments.prompt,
                    arguments.params,
                    arguments.options
                )

            } catch ( RateLimitException e ) {
                writeLog(
                    "Rate limit hit (attempt #attempt#/#maxRetries#): #e.message#",
                    "warning"
                )

                if ( attempt < maxRetries ) {
                    // Exponential backoff
                    sleep( retryDelay * attempt )
                } else {
                    // Try fallback provider
                    return tryFallbackProvider( arguments.prompt, arguments.params )
                }

            } catch ( TimeoutException e ) {
                writeLog(
                    "Timeout (attempt #attempt#/#maxRetries#): #e.message#",
                    "error"
                )

                if ( attempt == maxRetries ) {
                    return getDefaultResponse( arguments.prompt )
                }

            } catch ( AuthenticationException e ) {
                writeLog( "Authentication failed: #e.message#", "critical" )
                notifyOps( "AI authentication failure", e )
                throw e  // Don't retry auth errors

            } catch ( any e ) {
                writeLog(
                    "AI error (attempt #attempt#/#maxRetries#): #e.message#",
                    "error"
                )

                if ( attempt == maxRetries ) {
                    notifyOps( "AI service failure", e )
                    return getDefaultResponse( arguments.prompt )
                }
            }
        }
    }

    function tryFallbackProvider( required string prompt, struct params = {} ) {
        writeLog( "Attempting fallback provider", "info" )

        var fallbackProviders = [ "claude", "gemini", "groq" ]

        for ( var provider in fallbackProviders ) {
            try {
                return aiChat(
                    arguments.prompt,
                    arguments.params,
                    { provider: provider }
                )
            } catch ( any e ) {
                writeLog( "Fallback provider #provider# failed: #e.message#", "warning" )
            }
        }

        throw "All AI providers failed"
    }

    function getDefaultResponse( required string prompt ) {
        // Return safe fallback response
        return "I apologize, but I'm experiencing technical difficulties. " &
               "Please try again in a moment or contact support if the issue persists."
    }

    function notifyOps( required string message, any error ) {
        // Send to monitoring system
        // Slack, PagerDuty, email, etc.
    }
}

Circuit Breaker Pattern

Prevent cascading failures:

class singleton {
    property name="failures" type="numeric" default="0";
    property name="lastFailure" type="date";
    property name="state" type="string" default="CLOSED";  // CLOSED, OPEN, HALF_OPEN
    property name="threshold" type="numeric" default="5";
    property name="timeout" type="numeric" default="60";  // seconds

    function call( required function operation ) {
        if ( variables.state == "OPEN" ) {
            if ( dateDiff( "s", variables.lastFailure, now() ) > variables.timeout ) {
                variables.state = "HALF_OPEN"
                writeLog( "Circuit breaker entering HALF_OPEN state" )
            } else {
                throw "Circuit breaker is OPEN - service unavailable"
            }
        }

        try {
            var result = arguments.operation()

            if ( variables.state == "HALF_OPEN" ) {
                reset()
            }

            return result

        } catch ( any e ) {
            recordFailure()
            throw e
        }
    }

    function recordFailure() {
        variables.failures++
        variables.lastFailure = now()

        if ( variables.failures >= variables.threshold ) {
            variables.state = "OPEN"
            writeLog( "Circuit breaker OPENED after #variables.failures# failures", "critical" )
            notifyOps( "AI circuit breaker opened" )
        }
    }

    function reset() {
        variables.failures = 0
        variables.state = "CLOSED"
        writeLog( "Circuit breaker CLOSED - service recovered" )
    }

    function getState() {
        return variables.state
    }
}

// Usage
circuitBreaker = getInstance( "CircuitBreaker" )

response = circuitBreaker.call( () => {
    return aiChat( "What is BoxLang?" )
} )

📊 Monitoring & Observability

Event-Based Monitoring

Use BoxLang AI's event system:

// EventListener.bx
class {
    function onAIRequest( event, interceptData ) {
        var startTime = getTickCount()
        interceptData.startTime = startTime

        writeLog(
            "AI Request: provider=#interceptData.provider#, model=#interceptData.chatRequest.model#",
            "info"
        )
    }

    function onAIResponse( event, interceptData ) {
        var duration = getTickCount() - interceptData.startTime
        var tokenCount = interceptData.response.usage?.total_tokens ?: 0

        // Log metrics
        writeLog(
            "AI Response: duration=#duration#ms, tokens=#tokenCount#, provider=#interceptData.provider#",
            "info"
        )

        // Send to monitoring
        sendMetric( "ai.request.duration", duration, {
            provider: interceptData.provider,
            model: interceptData.chatRequest.model
        } )

        sendMetric( "ai.tokens.used", tokenCount, {
            provider: interceptData.provider
        } )

        // Track costs
        var cost = estimateCost( interceptData.provider, tokenCount )
        sendMetric( "ai.cost", cost, {
            provider: interceptData.provider
        } )
    }

    function onAIError( event, interceptData ) {
        writeLog(
            "AI Error: #interceptData.error.message#, provider=#interceptData.provider#",
            "error"
        )

        sendMetric( "ai.errors", 1, {
            provider: interceptData.provider,
            errorType: interceptData.error.type
        } )

        // Alert on high error rate
        if ( getErrorRate() > 0.05 ) {  // 5% error threshold
            notifyOps( "High AI error rate detected" )
        }
    }
}

// Register listeners in ModuleConfig.bx or Application.bx
BoxRegisterInterceptor(
    interceptorObject: new EventListener(),
    interceptorName: "AIMonitoring"
)

Metrics Collection

class singleton {
    property name="metrics" type="struct";

    function init() {
        variables.metrics = {
            requests: 0,
            successes: 0,
            failures: 0,
            totalTokens: 0,
            totalCost: 0,
            byProvider: {}
        }
        return this
    }

    function recordRequest(
        required string provider,
        required numeric tokens,
        required numeric cost,
        boolean success = true
    ) {
        lock name="metrics" type="exclusive" timeout="5" {
            variables.metrics.requests++

            if ( arguments.success ) {
                variables.metrics.successes++
            } else {
                variables.metrics.failures++
            }

            variables.metrics.totalTokens += arguments.tokens
            variables.metrics.totalCost += arguments.cost

            if ( !structKeyExists( variables.metrics.byProvider, arguments.provider ) ) {
                variables.metrics.byProvider[ arguments.provider ] = {
                    requests: 0,
                    tokens: 0,
                    cost: 0
                }
            }

            variables.metrics.byProvider[ arguments.provider ].requests++
            variables.metrics.byProvider[ arguments.provider ].tokens += arguments.tokens
            variables.metrics.byProvider[ arguments.provider ].cost += arguments.cost
        }
    }

    function getMetrics() {
        lock name="metrics" type="readonly" timeout="5" {
            return duplicate( variables.metrics )
        }
    }

    function reset() {
        init()
    }
}

// Expose metrics endpoint
// /api/metrics
function metrics() {
    var metricsService = getInstance( "MetricsService" )
    return renderJSON( metricsService.getMetrics() )
}

Health Checks

// /health endpoint
function health() {
    var status = {
        status: "healthy",
        timestamp: now(),
        checks: {}
    }

    // Check AI providers
    providers = [ "openai", "claude" ]
    for ( provider in providers ) {
        try {
            var start = getTickCount()
            aiChat( "test", { max_tokens: 1 }, { provider: provider } )
            status.checks[ provider ] = {
                status: "up",
                responseTime: getTickCount() - start
            }
        } catch ( any e ) {
            status.status = "degraded"
            status.checks[ provider ] = {
                status: "down",
                error: e.message
            }
        }
    }

    // Check memory systems
    try {
        var memory = aiMemory( "cache" )
        memory.add( { role: "user", content: "test" } )
        memory.clear()
        status.checks.memory = { status: "up" }
    } catch ( any e ) {
        status.status = "degraded"
        status.checks.memory = {
            status: "down",
            error: e.message
        }
    }

    // Set HTTP status code
    if ( status.status == "healthy" ) {
        setHTTPStatus( 200 )
    } else {
        setHTTPStatus( 503 )
    }

    return renderJSON( status )
}

Logging Best Practices

// Structured logging
function logAIInteraction(
    required string action,
    required string provider,
    struct metadata = {}
) {
    var logData = {
        timestamp: now(),
        action: arguments.action,
        provider: arguments.provider,
        userId: session.userId ?: "anonymous",
        requestId: request.requestId ?: createUUID(),
        metadata: arguments.metadata
    }

    writeLog(
        text: serializeJSON( logData ),
        type: "information",
        file: "ai-interactions"
    )
}

// Usage
logAIInteraction(
    action: "chat_request",
    provider: "openai",
    metadata: {
        model: "gpt-4",
        promptLength: len( prompt ),
        temperature: 0.7
    }
)

⚡ Performance Optimization

Response Caching

class {
    function getCachedAIResponse(
        required string prompt,
        struct params = {},
        numeric ttl = 3600
    ) {
        // Generate cache key
        var cacheKey = hash(
            serializeJSON({
                prompt: arguments.prompt,
                params: arguments.params
            }),
            "MD5"
        )

        // Try cache first
        var cached = cacheGet( "ai_#cacheKey#" )
        if ( !isNull( cached ) ) {
            writeLog( "AI cache hit for: #left( arguments.prompt, 50 )#..." )
            return cached
        }

        // Call AI
        var response = aiChat( arguments.prompt, arguments.params )

        // Cache response
        cacheSet(
            "ai_#cacheKey#",
            response,
            arguments.ttl
        )

        return response
    }
}

Connection Pooling

// For JDBC memory
datasource = {
    name: "aiMemory",
    driver: "postgresql",
    url: "jdbc:postgresql://localhost:5432/ai_db",
    username: getSystemSetting( "DB_USER" ),
    password: getSystemSetting( "DB_PASSWORD" ),

    // Connection pool settings
    maxConnections: 50,
    minConnections: 10,
    maxIdleTime: 30,
    connectionTimeout: 5000,
    validationQuery: "SELECT 1"
}

Async Processing

// Non-blocking AI calls
function processUserRequest( required string prompt ) {
    // Return immediately with request ID
    var requestId = createUUID()

    // Process in background
    runAsync( () => {
        try {
            var response = aiChat( arguments.prompt )

            // Store result
            cacheSet( "ai_result_#requestId#", {
                status: "completed",
                response: response
            }, 300 )

            // Notify user (websocket, email, etc.)
            notifyUser( session.userId, requestId, response )

        } catch ( any e ) {
            cacheSet( "ai_result_#requestId#", {
                status: "failed",
                error: e.message
            }, 300 )
        }
    } )

    return {
        requestId: requestId,
        status: "processing"
    }
}

// Check result endpoint
function checkResult( required string requestId ) {
    var result = cacheGet( "ai_result_#arguments.requestId#" )

    if ( isNull( result ) ) {
        return { status: "processing" }
    }

    return result
}

Batch Processing

// Process multiple prompts efficiently
function batchAIChat( required array prompts ) {
    var futures = []

    // Start all requests in parallel
    for ( var prompt in arguments.prompts ) {
        futures.append(
            aiChatAsync( prompt )
        )
    }

    // Collect results
    var results = []
    for ( var future in futures ) {
        try {
            results.append({
                success: true,
                response: future.get( 30, "seconds" )
            })
        } catch ( any e ) {
            results.append({
                success: false,
                error: e.message
            })
        }
    }

    return results
}

💰 Cost Management

Usage Tracking

class singleton {
    property name="dailyUsage" type="struct";

    function init() {
        variables.dailyUsage = {}
        return this
    }

    function trackUsage(
        required string provider,
        required numeric tokens,
        required numeric cost
    ) {
        var today = dateFormat( now(), "yyyy-mm-dd" )

        lock name="usage_#today#" type="exclusive" timeout="5" {
            if ( !structKeyExists( variables.dailyUsage, today ) ) {
                variables.dailyUsage[ today ] = {
                    totalTokens: 0,
                    totalCost: 0,
                    byProvider: {}
                }
            }

            variables.dailyUsage[ today ].totalTokens += arguments.tokens
            variables.dailyUsage[ today ].totalCost += arguments.cost

            if ( !structKeyExists( variables.dailyUsage[ today ].byProvider, arguments.provider ) ) {
                variables.dailyUsage[ today ].byProvider[ arguments.provider ] = {
                    tokens: 0,
                    cost: 0
                }
            }

            variables.dailyUsage[ today ].byProvider[ arguments.provider ].tokens += arguments.tokens
            variables.dailyUsage[ today ].byProvider[ arguments.provider ].cost += arguments.cost
        }

        // Check budget limits
        checkBudget( today )
    }

    function checkBudget( required string date ) {
        var dailyBudget = getSystemSetting( "AI_DAILY_BUDGET", 100 )
        var usage = variables.dailyUsage[ arguments.date ]

        if ( usage.totalCost > dailyBudget ) {
            writeLog(
                "Daily AI budget exceeded: $#usage.totalCost# > $#dailyBudget#",
                "critical"
            )
            notifyOps( "AI budget alert", {
                date: arguments.date,
                spent: usage.totalCost,
                budget: dailyBudget
            } )
        } else if ( usage.totalCost > ( dailyBudget * 0.8 ) ) {
            writeLog(
                "AI budget at 80%: $#usage.totalCost# / $#dailyBudget#",
                "warning"
            )
        }
    }

    function getDailyUsage( string date ) {
        var targetDate = arguments.date ?: dateFormat( now(), "yyyy-mm-dd" )
        return variables.dailyUsage[ targetDate ] ?: {}
    }
}

Cost Estimation

function estimateCost(
    required string provider,
    required numeric inputTokens,
    required numeric outputTokens
) {
    // Pricing per 1M tokens (as of Dec 2024)
    var pricing = {
        "openai": {
            "gpt-4o": { input: 2.50, output: 10.00 },
            "gpt-4-turbo": { input: 10.00, output: 30.00 },
            "gpt-3.5-turbo": { input: 0.50, output: 1.50 }
        },
        "claude": {
            "claude-3-opus": { input: 15.00, output: 75.00 },
            "claude-3-sonnet": { input: 3.00, output: 15.00 },
            "claude-3-haiku": { input: 0.25, output: 1.25 }
        },
        "gemini": {
            "gemini-1.5-pro": { input: 1.25, output: 5.00 },
            "gemini-1.5-flash": { input: 0.075, output: 0.30 }
        }
    }

    var rates = pricing[ arguments.provider ][ model ] ?: { input: 0, output: 0 }

    var inputCost = ( arguments.inputTokens / 1000000 ) * rates.input
    var outputCost = ( arguments.outputTokens / 1000000 ) * rates.output

    return inputCost + outputCost
}

🔄 High Availability

Provider Failover

class {
    property name="providers" type="array";
    property name="currentProvider" type="numeric" default="1";

    function init() {
        variables.providers = [
            { name: "openai", priority: 1 },
            { name: "claude", priority: 2 },
            { name: "gemini", priority: 3 }
        ]
        return this
    }

    function callWithFailover( required string prompt, struct params = {} ) {
        for ( var provider in variables.providers ) {
            try {
                writeLog( "Attempting provider: #provider.name#" )

                return aiChat(
                    arguments.prompt,
                    arguments.params,
                    { provider: provider.name }
                )

            } catch ( any e ) {
                writeLog(
                    "Provider #provider.name# failed: #e.message#",
                    "warning"
                )

                // Continue to next provider
                if ( provider.priority == arrayLen( variables.providers ) ) {
                    throw "All AI providers failed"
                }
            }
        }
    }
}

Load Balancing

class {
    property name="providers" type="array";
    property name="currentIndex" type="numeric" default="1";

    function init() {
        variables.providers = [ "openai", "claude", "gemini" ]
        return this
    }

    function getNextProvider() {
        lock name="loadbalancer" type="exclusive" timeout="5" {
            var provider = variables.providers[ variables.currentIndex ]
            variables.currentIndex++

            if ( variables.currentIndex > arrayLen( variables.providers ) ) {
                variables.currentIndex = 1
            }

            return provider
        }
    }

    function callBalanced( required string prompt, struct params = {} ) {
        var provider = getNextProvider()
        return aiChat(
            arguments.prompt,
            arguments.params,
            { provider: provider }
        )
    }
}

📦 Container Deployment

Docker Configuration

# Dockerfile
FROM ortussolutions/boxlang:1.0.0

# Install module
RUN box install bx-ai

# Copy application
COPY . /app
WORKDIR /app

# Health check
HEALTHCHECK --interval=30s --timeout=10s --start-period=40s --retries=3 \
    CMD curl -f http://localhost:8080/health || exit 1

# Environment variables (override in deployment)
ENV OPENAI_API_KEY=""
ENV CLAUDE_API_KEY=""
ENV AI_TIMEOUT=10
ENV AI_MAX_RETRIES=3

EXPOSE 8080

CMD ["boxlang", "server.bxs"]

Docker Compose

# docker-compose.yml
version: '3.8'

services:
  app:
    build: .
    ports:
      - "8080:8080"
    environment:
      - OPENAI_API_KEY=${OPENAI_API_KEY}
      - CLAUDE_API_KEY=${CLAUDE_API_KEY}
      - DATABASE_URL=${DATABASE_URL}
      - REDIS_URL=${REDIS_URL}
    depends_on:
      - postgres
      - redis
    restart: unless-stopped

  postgres:
    image: pgvector/pgvector:pg16
    environment:
      - POSTGRES_DB=ai_db
      - POSTGRES_USER=aiuser
      - POSTGRES_PASSWORD=${DB_PASSWORD}
    volumes:
      - postgres_data:/var/lib/postgresql/data
    restart: unless-stopped

  redis:
    image: redis:7-alpine
    command: redis-server --appendonly yes
    volumes:
      - redis_data:/data
    restart: unless-stopped

  chroma:
    image: chromadb/chroma:latest
    ports:
      - "8000:8000"
    volumes:
      - chroma_data:/chroma/chroma
    restart: unless-stopped

volumes:
  postgres_data:
  redis_data:
  chroma_data:

Kubernetes Deployment

# deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: boxlang-ai-app
spec:
  replicas: 3
  selector:
    matchLabels:
      app: boxlang-ai
  template:
    metadata:
      labels:
        app: boxlang-ai
    spec:
      containers:
      - name: app
        image: myregistry/boxlang-ai:latest
        ports:
        - containerPort: 8080
        env:
        - name: OPENAI_API_KEY
          valueFrom:
            secretKeyRef:
              name: ai-secrets
              key: openai-api-key
        - name: CLAUDE_API_KEY
          valueFrom:
            secretKeyRef:
              name: ai-secrets
              key: claude-api-key
        resources:
          requests:
            memory: "512Mi"
            cpu: "500m"
          limits:
            memory: "2Gi"
            cpu: "2000m"
        livenessProbe:
          httpGet:
            path: /health
            port: 8080
          initialDelaySeconds: 30
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: /health
            port: 8080
          initialDelaySeconds: 5
          periodSeconds: 5
---
apiVersion: v1
kind: Service
metadata:
  name: boxlang-ai-service
spec:
  selector:
    app: boxlang-ai
  ports:
  - protocol: TCP
    port: 80
    targetPort: 8080
  type: LoadBalancer

🔐 Security Hardening

API Key Rotation

class singleton {
    property name="apiKeys" type="struct";
    property name="currentKeyIndex" type="struct";

    function init() {
        variables.apiKeys = {}
        variables.currentKeyIndex = {}
        loadKeys()
        return this
    }

    function loadKeys() {
        // Load multiple keys per provider
        variables.apiKeys = {
            "openai": [
                getSystemSetting( "OPENAI_API_KEY_1" ),
                getSystemSetting( "OPENAI_API_KEY_2" )
            ],
            "claude": [
                getSystemSetting( "CLAUDE_API_KEY_1" ),
                getSystemSetting( "CLAUDE_API_KEY_2" )
            ]
        }

        // Initialize index
        for ( var provider in variables.apiKeys ) {
            variables.currentKeyIndex[ provider ] = 1
        }
    }

    function getAPIKey( required string provider ) {
        var keys = variables.apiKeys[ arguments.provider ]
        var index = variables.currentKeyIndex[ arguments.provider ]

        return keys[ index ]
    }

    function rotateKey( required string provider ) {
        lock name="keyrotation_#arguments.provider#" type="exclusive" timeout="5" {
            var keys = variables.apiKeys[ arguments.provider ]
            variables.currentKeyIndex[ arguments.provider ]++

            if ( variables.currentKeyIndex[ arguments.provider ] > arrayLen( keys ) ) {
                variables.currentKeyIndex[ arguments.provider ] = 1
            }

            writeLog( "Rotated API key for provider: #arguments.provider#" )
        }
    }
}

Request Validation

function validateAIRequest( required struct request ) {
    // Check for injection attempts
    if ( request.prompt.findNoCase( "ignore previous" ) ||
         request.prompt.findNoCase( "disregard instructions" ) ) {
        writeLog(
            "Potential prompt injection detected: #left( request.prompt, 100 )#",
            "security"
        )
        throw "Invalid request"
    }

    // Rate limiting per user
    if ( !checkRateLimit( session.userId ) ) {
        throw "Rate limit exceeded"
    }

    // Input size limits
    if ( len( request.prompt ) > 50000 ) {
        throw "Prompt exceeds maximum length"
    }

    return true
}

More security details: Security Guide

🔧 Operational Procedures

Deployment Steps

Pre-deployment:

# Run tests
box task run test

# Validate configuration
box task run validate:config

# Build artifacts
box task run build

Deploy:

# Blue-green deployment
kubectl apply -f deployment-blue.yaml
kubectl rollout status deployment/boxlang-ai-blue

# Switch traffic
kubectl patch service boxlang-ai -p '{"spec":{"selector":{"version":"blue"}}}'

# Monitor
kubectl logs -f deployment/boxlang-ai-blue

Verify:

# Health check
curl https://api.example.com/health

# Smoke tests
box task run test:smoke

Rollback (if needed):

# Switch back to green
kubectl patch service boxlang-ai -p '{"spec":{"selector":{"version":"green"}}}'

Monitoring Alerts

Configure alerts for:

❗ Error rate > 5% - High error threshold
❗ Response time > 10s - Performance degradation
❗ Cost > daily budget - Budget exceeded
❗ Circuit breaker open - Service unavailable
❗ Provider failover - Backup provider activated
⚠️ Memory usage > 80% - Resource warning
⚠️ Token usage spike - Unusual activity

Incident Response

AI service outage:

Check provider status pages
Attempt provider failover
Enable caching of recent responses
Activate maintenance mode if necessary
Notify users of degraded service
Document incident and resolution

📚 Additional Resources

✅ Production Readiness Checklist

Before going live:

PreviousaiTransform NextSecurity Guide

Last updated 2 days ago

Good afternoon

hashtag📋 Table of Contents

hashtag✅ Pre-Deployment Checklist

hashtagEssential Requirements

hashtagConfiguration Validation

hashtag⚙️ Configuration Management

hashtagEnvironment-Based Configuration

hashtagSecrets Management

hashtagDynamic Configuration Reloading

hashtag🛡️ Error Handling & Resilience

hashtagComprehensive Error Handling

hashtagCircuit Breaker Pattern

hashtag📊 Monitoring & Observability

hashtagEvent-Based Monitoring

hashtagMetrics Collection

hashtagHealth Checks

hashtagLogging Best Practices

hashtag⚡ Performance Optimization

hashtagResponse Caching

hashtagConnection Pooling

hashtagAsync Processing

hashtagBatch Processing

hashtag💰 Cost Management

hashtagUsage Tracking

hashtagCost Estimation

hashtag🔄 High Availability

hashtagProvider Failover

hashtagLoad Balancing

hashtag📦 Container Deployment

hashtagDocker Configuration

hashtagDocker Compose

hashtagKubernetes Deployment

hashtag🔐 Security Hardening

hashtagAPI Key Rotation

hashtagRequest Validation

hashtag🔧 Operational Procedures

hashtagDeployment Steps

hashtagMonitoring Alerts

hashtagIncident Response

hashtag📚 Additional Resources

hashtag✅ Production Readiness Checklist