{
  "routingPolicy": {
    "defaultModel": "gpt-4",
    "rules": [
      {
        "condition": "tokenCount > 10000",
        "targetModel": "gpt-5",
        "priority": 1
      },
      {
        "condition": "latencyRequirement < 500ms",
        "targetModel": "gpt-3.5-turbo",
        "priority": 2
      }
    ]
  }
}

# Prerequisites prüfen
Get-AzContext
Get-AzSubscription

# Resource Group für AI Foundry erstellen
$resourceGroup = "rg-ai-foundry-prod-we"
$location = "West Europe"

New-AzResourceGroup -Name $resourceGroup -Location $location

# Azure AI Services aktivieren
Register-AzResourceProvider -ProviderNamespace "Microsoft.CognitiveServices"

// ai-foundry-workspace.bicep
param workspaceName string
param location string = resourceGroup().location
param keyVaultName string
param applicationInsightsName string

resource aiFoundryWorkspace 'Microsoft.MachineLearningServices/workspaces@2023-08-01-preview' = {
  name: workspaceName
  location: location
  properties: {
    friendlyName: 'Enterprise AI Foundry'
    keyVault: keyVaultName
    applicationInsights: applicationInsightsName
    publicNetworkAccess: 'Disabled'
    managedNetwork: {
      isolationMode: 'AllowOnlyApprovedOutbound'
    }
  }
  identity: {
    type: 'SystemAssigned'
  }
}

# Model Router Service erstellen
az ml compute create \\
  --resource-group $resourceGroup \\
  --workspace-name "ai-foundry-ws" \\
  --name "model-router-endpoint" \\
  --type "managed-online-endpoint"

# Routing-Regeln definieren
az ml online-deployment create \\
  --resource-group $resourceGroup \\
  --workspace-name "ai-foundry-ws" \\
  --endpoint-name "model-router-endpoint" \\
  --name "gpt-5-deployment" \\
  --model azureml://registries/azure-openai/models/gpt-5:1

{
  "dataProcessingLocation": "EU",
  "encryptionConfig": {
    "atRest": {
      "provider": "Customer-Managed-Keys",
      "keyVaultUri": "<https://kv-ai-prod-we.vault.azure.net/>"
    },
    "inTransit": "TLS 1.3"
  },
  "dataRetention": {
    "logData": "90 days",
    "trainingData": "No retention",
    "personalData": "On-demand deletion"
  }
}

resource privateEndpoint 'Microsoft.Network/privateEndpoints@2023-04-01' = {
  name: 'pe-ai-foundry'
  location: location
  properties: {
    subnet: {
      id: subnetId
    }
    privateLinkServiceConnections: [{
      name: 'ai-foundry-connection'
      properties: {
        privateLinkServiceId: aiFoundryWorkspace.id
        groupIds: ['amlworkspace']
      }
    }]
  }
}

# Cost Analysis für AI Workloads
$subscription = "your-subscription-id"
$resourceGroup = "rg-ai-foundry-prod-we"

# Monatliche Kosten nach Service aggregieren
Get-AzConsumptionUsageDetail -BillingPeriodName latest | 
    Where-Object {$_.ResourceGroup -eq $resourceGroup} |
    Group-Object ResourceType |
    Select-Object Name, @{Name="Cost";Expression={($_.Group | Measure-Object PreTaxCost -Sum).Sum}}

{
  "budgetConfig": {
    "monthlyLimit": 12000,
    "alerts": [
      {
        "threshold": 50,
        "emails": ["cio@company.de", "it-ops@company.de"]
 

     },
      {
        "threshold": 80,
        "action": "scale-down-non-critical-workloads"
      },
      {
        "threshold": 95,
        "action": "emergency-cost-controls"
      }
    ]
  }
}

# Migration Runbook Template
migration_checklist:
  pre_migration:
    - data_quality_assessment: required
    - performance_baseline: required
    - rollback_plan: required
    - stakeholder_communication: required
  
  duri

ng_migration:
    - parallel_validation: continuous
    - error_monitoring: real_time
    - user_feedback: daily
    
  post_migration:
    - performance_validation: required
    - cost_optimization: required
    - documentation_update: required

{
  "sapIntegration": {
    "protocol": "RFC/REST",
    "authenticationMethod": "OAuth 2.0",
    "dataFlow": {
      "inbound": "SAP → Azure AI Foundry → GPT-5 Analysis",
      "outbound": "AI Insights → SAP Business Objects"
    },
    "useCase": "Purchase Order Intelligence"
  }
}

# Power Automate Flow für AI-gestützte E-Mail-Verarbeitung
$flowDefinition = @{
    "triggers" = @{
        "email_received" = @{
            "type" = "outlook"
            "conditions" = @("sender_domain" = "customer.de")
        }
    }
    "actions" = @{
        "analyze_sentiment" = @{
            "type" = "azure_ai_foundry"
            "model" = "gpt-5"
            "prompt" = "Analyze customer sentiment and categorize urgency"
        }
        "create_case" = @{
            "type" = "dynamics_365"
            "priority" = "{{sentiment_analysis.urgency}}"
        }
    }
}

contract_analysis_agent:
  input: PDF/Word Contracts
  processing:
    - document_extraction: Azure Document Intelligence
    - legal_analysis: GPT-5 with legal prompt templates  
    - risk_assessment: Custom scoring model
    - compliance_check: GDPR/German legal framework
  output:
    - executive_summary: Key terms and risks
    - action_items: Required reviews and approvals
    - compliance_score: 1-10 scale

# Simplified Agent Logic für Predictive Maintenance
maintenance_agent = {
    "data_sources": [
        "iot_sensors",  # Temperatur, Vibration, Strom
        "sap_maintenance_history",
        "production_schedule"
    ],
    "ai_models": {
        "anomaly_detection": "azure_ml_automl",
        "failure_prediction": "gpt-5_reasoning",
        "optimization": "custom_regression_model"
    },
    "actions": {
        "alert_threshold": 0.85,
        "maintenance_scheduling": "automatic",
        "spare_parts_ordering": "predictive"
    }
}

{
  "customer_support_agent": {
    "channels": ["email", "chat", "phone_transcription"],
    "classification": {
      "model": "gpt-5",
      "categories": ["technical", "billing", "returns", "general"],
      "confidence_threshold": 0.9
    },
    "resolution": {
      "level1": "automated_response",
      "level2": "agent_assistance", 
      "level3": "escalation_to_human"
    },
    "knowledge_base": {
      "source": "sharepoint_online",
      "update_frequency": "real_time",
      "vector_search": "azure_cognitive_search"
    }
  }
}

// main.bicep - Complete AI Foundry Infrastructure
@description('Environment name (dev, test, prod)')
param environment string = 'prod'

@description('Location for resources')
param location string = resourceGroup().location

@description('Company prefix for resource naming')
param companyPrefix string = 'contoso'

// Variables
var namingPrefix = '${companyPrefix}-ai-${environment}'
var tags = {
  Environment: environment
  Project: 'AI-Foundry'
  CostCenter: 'IT-Innovation' 
  DataClassification: 'Internal'
}

// Storage Account für AI Workloads
resource storageAccount 'Microsoft.Storage/storageAccounts@2023-01-01' = {
  name: '${replace(namingPrefix, '-', '')}storage'
  location: location
  tags: tags
  sku: {
    name: 'Standard_LRS'
  }
  kind: 'StorageV2'
  properties: {
    publicNetworkAccess: 'Disabled'
    supportsHttpsTrafficOnly: true
    encryption: {
      services: {
        blob: {
          enabled: true
          keyType: 'Account'
        }
        file: {
          enabled: true
          keyType: 'Account'  
        }
      }
      keySource: 'Microsoft.Storage'
    }
    networkAcls: {
      defaultAction: 'Deny'
      bypass: 'AzureServices'
    }
  }
}

// Key Vault für Secrets Management
resource keyVault 'Microsoft.KeyVault/vaults@2023-07-01' = {
  name: '${namingPrefix}-kv'
  location: location
  tags: tags
  properties: {
    tenantId: subscription().tenantId
    sku: {
      family: 'A'
      name: 'standard'
    }
    publicNetworkAccess: 'Disabled'
    enableRbacAuthorization: true
    enableSoftDelete: true
    softDeleteRetentionInDays: 90
    enablePurgeProtection: true
  }
}

// Application Insights für KI-Monitoring
resource applicationInsights 'Microsoft.Insights/components@2020-02-02' = {
  name: '${namingPrefix}-ai-insights'
  location: location
  tags: tags
  kind: 'web'
  properties: {
    Application_Type: 'web'
    publicNetworkAccessForIngestion: 'Disabled'
    publicNetworkAccessForQuery: 'Disabled'
  }
}

// Machine Learning Workspace (AI Foundry)
resource mlWorkspace 'Microsoft.MachineLearningServices/workspaces@2023-10-01' = {
  name: '${namingPrefix}-mlws'
  location: location
  tags: tags
  identity: {
    type: 'SystemAssigned'
  }
  properties: {
    friendlyName: 'Enterprise AI Foundry Workspace'
    storageAccount: storageAccount.id
    keyVault: keyVault.id
    applicationInsights: applicationInsights.id
    publicNetworkAccess: 'Disabled'
    managedNetwork: {
      isolationMode: 'AllowOnlyApprovedOutbound'
      outboundRules: {
        'allow-azure-ai-services': {
          type: 'ServiceTag'
          destination: 'CognitiveServices'
          status: 'Active'
        }
      }
    }
  }
}

// Cognitive Services Account für OpenAI
resource cognitiveServices 'Microsoft.CognitiveServices/accounts@2023-10-01-preview' = {
  name: '${namingPrefix}-openai'
  location: location
  tags: tags
  sku: {
    name: 'S0'
  }
  kind: 'OpenAI'
  properties: {
    publicNetworkAccess: 'D

isabled'
    restrictOutboundNetworkAccess: true
    customSubDomainName: '${namingPrefix}-openai'
  }
}

// Output wichtiger Resource IDs
output workspaceId string = mlWorkspace.id
output cognitiveServicesEndpoint string = cognitiveServices.properties.endpoint
output storageAccountName string = storageAccount.name

# deploy-model-router.ps1
param(
    [Parameter(Mandatory=$true)]
    [string]$SubscriptionId,
    
    [Parameter(Mandatory=$true)]
    [string]$ResourceGroupName,
    
    [Parameter(Mandatory=$true)]
    [string]$WorkspaceName,
    
    [Parameter(Mandatory=$false)]
    [string]$Location = "West Europe"
)

# Prerequisites Check
Write-Host "Checking prerequisites..." -ForegroundColor Green
$modules = @('Az.Accounts', 'Az.MachineLearning', 'Az.CognitiveServices')
foreach ($module in $modules) {
    if (-not (Get-Module -ListAvailable -Name $module)) {
        Write-Host "Installing module $module..." -ForegroundColor Yellow
        Install-Module -Name $module -Force -Scope CurrentUser
    }
}

# Azure Login und Subscription Setting
Connect-AzAccount
Set-AzContext -SubscriptionId $SubscriptionId

# Model Router Endpoint erstellen
$endpointConfig = @{
    ResourceGroupName = $ResourceGroupName
    WorkspaceName = $WorkspaceName
    Name = "model-router-endpoint"
    EndpointType = "online"
    AuthMode = "key"
    Properties = @{
        description = "Enterprise Model Router für GPT-5 und andere Modelle"
        tags = @{
            environment = "production"
            purpose = "model-routing"
            compliance = "gdpr-compliant"
        }
    }
}

Write-Host "Creating Model Router Endpoint..." -ForegroundColor Green
$endpoint = New-AzMLOnlineEndpoint @endpointConfig

# GPT-5 Deployment konfigurieren
$gpt5DeploymentConfig = @{
    ResourceGroupName = $ResourceGroupName
    WorkspaceName = $WorkspaceName
    EndpointName = "model-router-endpoint"
    Name = "gpt-5-deployment"
    Model =

 "gpt-5"
    InstanceType = "Standard_DS3_v2"
    InstanceCount = 2
    RequestSettings = @{
        requestTimeoutMs = 30000
        maxQueueWait = 500
    }
    EnvironmentVariables = @{
        AZURE_OPENAI_ENDPOINT = "{endpoint_url}"
        AZURE_OPENAI_API_VERSION = "2024-02-01"
        ENABLE_CONTENT_FILTER = "true"
    }
}

Write-Host "Deploying GPT-5 Model..." -ForegroundColor Green  
$deployment = New-AzMLOnlineDeployment @gpt5DeploymentConfig

# Traffic-Routing konfigurieren (100% auf GPT-5 initially)
$trafficConfig = @{
    ResourceGroupName = $ResourceGroupName
    WorkspaceName = $WorkspaceName
    EndpointName = "model-router-endpoint"
    Traffic = @{
        "gpt-5-deployment" = 100
    }
}

Write-Host "Configuring traffic routing..." -ForegroundColor Green
Set-AzMLOnlineEndpointTraffic @trafficConfig

# Validation und Testing
Write-Host "Validating deployment..." -ForegroundColor Green
$endpointStatus = Get-AzMLOnlineEndpoint -ResourceGroupName $ResourceGroupName -WorkspaceName $WorkspaceName -Name "model-router-endpoint"

if ($endpointStatus.ProvisioningState -eq "Succeeded") {
    Write-Host "✓ Model Router successfully deployed!" -ForegroundColor Green
    Write-Host "Endpoint URL: $($endpointStatus.ScoringUri)" -ForegroundColor Cyan
    
    # Test-Anfrage senden
    $testPayload = @{
        messages = @(
            @{
                role = "user"
                content = "Test der Model Router Funktionalität für deutsche Unternehmen."
            }
        )
        max_tokens = 100
        temperature = 0.7
    } | ConvertTo-Json -Depth 3
    
    Write-Host "Testing endpoint with sample request..." -ForegroundColor Yellow
    # Note: Actual invoke would require API key retrieval and HTTP request
    Write-Host "Test payload prepared. Use endpoint URL for testing." -ForegroundColor Cyan
} else {
    Write-Error "Deployment failed. Status: $($endpointStatus.ProvisioningState)"
}

# 

ai_agent_framework.py - Enterprise AI Agent Base Class
import os
import asyncio
from typing import Dict, List, Any, Optional
from dataclasses import dataclass
from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential
from azure.storage.blob import BlobServiceClient
import openai
import logging

@dataclass
class AgentConfig:
    name: str
    description: str
    model: str = "gpt-5"
    temperature: float = 0.7
    max_tokens: int = 2000
    system_prompt: str = ""
    tools: List[str] = None
    compliance_level: str = "GDPR"

class EnterpriseAIAgent:
    """
    Base class für Enterprise AI Agents mit Compliance, Monitoring und Error Handling
    """
    
    def __init__(self, config: AgentConfig, workspace_config: Dict[str, str]):
        self.config = config
        self.workspace_config = workspace_config
        self.credential = DefaultAzureCredential()
        
        # Initialize Azure ML Client
        self.ml_client = MLClient(
            credential=self.credential,
            subscription_id=workspace_config["subscription_id"],
            resource_group_name=workspace_config["resource_group"],
            workspace_name=workspace_config["workspace_name"]
        )
        
        # Initialize OpenAI Client für Azure
        openai.api_type = "azure"
        openai.api_base = workspace_config["openai_endpoint"]
        openai.api_version = "2024-02-15-preview"
        openai.api_key = self._get_api_key()
        
        # Logging setup
        self.logger = self._setup_logging()
        
    def _get_api_key(self) -> str:
        """Retrieve API key from Azure Key Vault"""
        from azure.keyvault.secrets import SecretClient
        
        kv_client = SecretClient(
            vault_url=self.workspace_config["key_vault_url"],
            credential=self.credential
        )
        return kv_client.get_secret("openai-api-key").value
        
    def _setup_logging(self) -> logging.Logger:
        """Configure structure

d logging für Compliance"""
        logger = logging.getLogger(f"AIAgent-{self.config.name}")
        logger.setLevel(logging.INFO)
        
        # Custom formatter für GDPR Compliance
        formatter = logging.Formatter(
            '%(asctime)s - %(name)s - %(levelname)s - '
            '[Agent: %(agent_name)s] [SessionId: %(session_id)s] - %(message)s'
        )
        
        handler = logging.StreamHandler()
        handler.setFormatter(formatter)
        logger.addHandler(handler)
        
        return logger
    
    async def process_request(self, 
                            user_input: str, 
                            context: Dict[str, Any] = None,
                            session_id: str = None) -> Dict[str, Any]:
        """
        Hauptmethode für Agent-Request Processing
        
        Args:
            user_input: User query oder command
            context: Additional context data 
            session_id: Session identifier für Tracking
            
        Returns:
            Dict mit response, confidence, metadata
        """
        
        start_time = asyncio.get_event_loop().time()
        
        try:
            # Input Validation und Sanitization
            if not self._validate_input(user_input):
                raise ValueError("Input validation failed")
            
            # GDPR Compliance Check
            personal_data_detected = self._detect_personal_data(user_input)
            if personal_data_detected and self.config.compliance_level == "GDPR":
                self.logger.warning(
                    "Personal data detected in input", 
                    extra={"session_id": session_id, "agent_name": self.config.name}
                )
            
            # Model Router Call
            response = await self._call_model_router(
                user_input, 
                context=context,
                session_id=session_id
            )
            
            # Response Post-Processing
         

   processed_response = self._post_process_response(response)
            
            # Metadata für Monitoring
            processing_time = asyncio.get_event_loop().time() - start_time
            metadata = {
                "processing_time_ms": processing_time * 1000,
                "model_used": response.get("model", self.config.model),
                "token_usage": response.get("usage", {}),
                "confidence_score": self._calculate_confidence(response),
                "session_id": session_id,
                "compliance_flags": {
                    "personal_data_detected": personal_data_detected,
                    "content_filtered": response.get("content_filter_applied", False)
                }
            }
            
            self.logger.info(
                f"Request processed successfully in {processing_time*1000:.2f}ms",
                extra={"session_id": session_id, "agent_name": self.config.name}
            )
            
            return {
                "response": processed_response,
                "metadata": metadata,
                "status": "success"
            }
            
        except Exception as e:
            self.logger.error(
                f"Error processing request: {str(e)}",
                extra={"session_id": session_id, "agent_name": self.config.name},
                exc_info=True
            )
            return {
                "response": "Entschuldigung, bei der Verarbeitung ist ein Fehler aufgetreten.",
                "metadata": {"error": str(e), "session_id": session_id},
                "status": "error"
            }
    
    async def _call_model_router(self, 
                               user_input: str, 
                               context: Dict[str, Any] = None,
                               session_id: str = None) -> Dict[str, Any]:
        """Call Azure Model Router mit intelligent routing"""
        
        messages = [
            {"role": "system", "content": sel

f.config.system_prompt}
        ]
        
        if context:
            context_prompt = self._format_context(context)
            messages.append({"role": "system", "content": f"Context: {context_prompt}"})
        
        messages.append({"role": "user", "content": user_input})
        
        # Model Router Request
        response = await openai.ChatCompletion.acreate(
            engine=self.config.model,
            messages=messages,
            temperature=self.config.temperature,
            max_tokens=self.config.max_tokens,
            top_p=0.95,
            frequency_penalty=0,
            presence_penalty=0,
            stop=None,
            user=session_id  # Für Usage Tracking
        )
        
        return {
            "content": response.choices[0].message.content,
            "model": response.model,
            "usage": response.usage,
            "finish_reason": response.choices[0].finish_reason
        }
    
    def _validate_input(self, user_input: str) -> bool:
        """Input validation für Security"""
        if not user_input or len(user_input.strip()) == 0:
            return False
        if len(user_input) > 10000:  # Max input length
            return False
        # Add more validation logic as needed
        return True
    
    def _detect_personal_data(self, text: str) -> bool:
        """Simplified personal data detection für GDPR"""
        import re
        
        # Email pattern
        email_pattern = r'\\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\\.[A-Z|a-z]{2,}\\b'
        if re.search(email_pattern, text):
            return True
        
        # German phone number pattern  
        phone_pattern = r'(\\+49|0)[1-9][0-9]{1,14}'
        if re.search(phone_pattern, text):
            return True
        
        # IBAN pattern
        iban_pattern = r'DE\\d{20}'
        if re.search(iban_pattern, text):
            return True
            
        return False
    
    def _post_process_response(self, response: Dict[str, 

Any]) -> str:
        """Post-process model response für Consistency"""
        content = response.get("content", "")
        
        # Remove potential hallucinated URLs
        import re
        url_pattern = r'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\\\(\\\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+'
        content = re.sub(url_pattern, "[URL entfernt aus Sicherheitsgründen]", content)
        
        # Add standard disclaimer for AI-generated content
        if self.config.compliance_level == "GDPR":
            content += "\\n\\n*Hinweis: Diese Antwort wurde KI-generiert. Bitte prüfen Sie wichtige Informationen.*"
        
        return content
    
    def _calculate_confidence(self, response: Dict[str, Any]) -> float:
        """Calculate confidence score based on response characteristics"""
        # Simplified confidence calculation
        finish_reason = response.get("finish_reason")
        content_length = len(response.get("content", ""))
        
        if finish_reason == "stop" and content_length > 50:
            return 0.9
        elif finish_reason == "length":
            return 0.7
        else:
            return 0.5
    
    def _format_context(self, context: Dict[str, Any]) -> str:
        """Format context data for model consumption"""
        formatted_context = []
        for key, value in context.items():
            formatted_context.append(f"{key}: {value}")
        return "\\n".join(formatted_context)

# Beispiel-Implementierung: Contract Analysis Agent
class ContractAnalysisAgent(EnterpriseAIAgent):
    """Specialized Agent für Legal Contract Analysis"""
    
    def __init__(self, workspace_config: Dict[str, str]):
        config = AgentConfig(
            name="ContractAnalyzer",
            description="Analyzes legal contracts for German law compliance",
            model="gpt-5",
            temperature=0.3,  # Lower temperature für factual analysis
            system_prompt="""Du bist ein spezialisierter KI-Assistent für deutsche Vertra

gsanalyse.
            Analysiere Verträge auf:
            - DSGVO Compliance
            - Rechtliche Risiken
            - Wichtige Klauseln und Fristen
            - Verbesserungsvorschläge
            
            Antworte präzise und strukturiert. Gebe keine Rechtsberatung.""",
            compliance_level="GDPR"
        )
        super().__init__(config, workspace_config)
    
    async def analyze_contract(self, 
                             contract_text: str, 
                             contract_type: str = "general") -> Dict[str, Any]:
        """Specialized method für contract analysis"""
        
        context = {
            "contract_type": contract_type,
            "analysis_date": "2024-01-15",
            "jurisdiction": "Deutschland"
        }
        
        prompt = f"""Analysiere den folgenden {contract_type} Vertrag:

{contract_text}

Erwartete Analyse:
1. DSGVO Compliance Status
2. Identifizierte Risiken (Hoch/Mittel/Niedrig)
3. Wichtige Klauseln und Fristen
4. Verbesserungsvorschläge
5. Zusammenfassung für Management

Strukturiere die Antwort klar und verwende deutsche Rechtsterminologie."""

        return await self.process_request(
            user_input=prompt,
            context=context  
        )

# Example Usage
if __name__ == "__main__":
    
    # Workspace Configuration
    workspace_config = {
        "subscription_id": "your-subscription-id",
        "resource_group": "rg-ai-foundry-prod-we", 
        "workspace_name": "contoso-ai-prod-mlws",
        "openai_endpoint": "<https://contoso-ai-prod-openai.openai.azure.com/>",
        "key_vault_url": "<https://contoso-ai-prod-kv.vault.azure.net/>"
    }
    
    # Initialize Contract Analysis Agent
    contract_agent = ContractAnalysisAgent(workspace_config)
    
    # Example contract analysis
    sample_contract = """
    MUSTERVERTRAG
    
    Zwischen der Musterfirma GmbH und dem Auftragnehmer...
    [Rest des Vertrags würde hier stehen]
    """
    
    # Run analysis (in re

al implementation, use asyncio.run())
    # result = await contract_agent.analyze_contract(sample_contract, "Dienstleistungsvertrag")
    print("Contract Analysis Agent initialized successfully!")

{
  "monitoring_config": {
    "metrics": {
      "response_time_p95": {
        "threshold": "3000ms",
        "action": "scale_up_instances"
      },
      "error_rate": {
        "threshold": "5%",
        "action": "trigger_incident"
      },
      "cost_burn_rate": {
        "threshold": "120% of budget",
        "action": "enable_cost_controls"
      }
    },
    "log_aggregation": {
      "retention": "30 days",
      "pii_filtering": true,
      "export_to": "azure_monitor_logs"
    }
  }
}

# auto-remediation.ps1 - Automated incident response
param(
    [Parameter(Mandatory=$true)]
    [string]$AlertType,
    
    [Parameter(Mandatory=$true)] 
    [string]$Severity
)

switch ($AlertType) {
    "HighLatency" {
        Write-Host "Detecting high latency issue..." -ForegroundColor Yellow
        
        # Scale up compute instances
        az ml online-endpoint update `
            --resource-group "rg-ai-foundry-prod-we" `
            --workspace-name "contoso-ai-prod-mlws" `
            --name "model-router-endpoint" `
    

        --deployment-name "gpt-5-deployment" `
            --instance-count 4
            
        Write-Host "Scaled up instances to handle increased load" -ForegroundColor Green
    }
    
    "HighErrorRate" {
        Write-Host "High error rate detected - implementing circuit breaker..." -ForegroundColor Red
        
        # Switch traffic to backup model
        az ml online-endpoint update `
            --resource-group "rg-ai-foundry-prod-we" `
            --workspace-name "contoso-ai-prod-mlws" `
            --name "model-router-endpoint" `
            --traffic "gpt-5-deployment=50,gpt-4-backup=50"
            
        # Trigger incident in ServiceNow/ITSM
        Invoke-RestMethod -Uri "<https://company.service-now.com/api/incidents>" `
            -Method POST `
            -Headers @{"Authorization"="Bearer $env:SERVICENOW_TOKEN"} `
            -Body (@{
                "short_description" = "Azure AI Foundry High Error Rate"
                "urgency" = $Severity
                "assignment_group" = "IT-AI-Operations"
            } | ConvertTo-Json)
    }
    
    "CostOverrun" {
        Write-Host "Cost overrun detected - enabling cost controls..." -ForegroundColor Orange
        
        # Reduce instance count für non-critical workloads
        $nonCriticalDeployments = @("dev-gpt-deployment", "test-model-deployment")
        
        foreach ($deployment in $nonCriticalDeployments) {
            az ml online-endpoint update `
                --resource-group "rg-ai-foundry-prod-we" `
                --workspace-name "contoso-ai-prod-mlws" `
                --name "model-router-endpoint" `
                --deployment-name $deployment `
                --instance-count 1
        }
        
        Write-Host "Reduced non-critical deployments to minimum instances" -ForegroundColor Green
    }
}

disaster_recovery:
  primary_region: "West Europe"
  secondary_

region: "North Europe"
  failover_triggers:
    - region_outage: automatic
    - performance_degradation: manual_approval
    - compliance_issue: immediate
  
  recovery_time_objective: "15 minutes"
  recovery_point_objective: "5 minutes"
  
  backup_strategy:
    model_configurations: daily_backup
    training_data: continuous_replication
    user_sessions: redis_geo_replication

graph TD
    A[Bestehende IT-Landschaft] --> B{Platform Evaluation}
    B --> C[Azure AI Foundry]
    B --> D[AWS Bedrock] 
    B --> E[Google Vertex AI]
    
    C --> F[Low Migration Risk<br/>Native Microsoft Integration]
    D --> G[Medium Risk<br/>Service Translation Needed]
    E --> H[High Risk<br/>Complete Platform Change]
    
    F --> I[4-6 Monate Implementation]
    G --> J[8-12 Monate Implementation] 
    H --> K[12-18 Monate Implementation]

initial_investment:
  azure_ai_foundry_setup:
    infrastructure: 45000  # EUR
    professional_services: 85000  # EUR
    training_and_certification: 25000  # EUR
  
  change_management:
    project_management: 40000  # EUR
    stakeholder_training: 15000  # EUR
    communication_campaign: 8000  # EUR
  
  integration_costs:
    sap_connectors: 35000  # EUR
    custom_development: 60000  # EUR
    testing_and_validation: 20000  # EUR
    
  total_year_1: 333000  # EUR

Jahr 1: Investment €333.000, Betrieb €276.000, Nutzen €850.000 = -€241.000
Jahr 2: Investment €0, Betrieb €325.000, Nutzen €2.100.000 = +€1.534.000
Jahr 3: Investment €0, Betrieb €380.000, Nutzen €2.650.000 = +€3.804.000
Jahr 4: Investment €0, Betrieb €420.000, Nutzen €3.100.000 = +€6.484.000  
Jahr 5: Investment €0, Betrieb €465.000, Nutzen €3.400.000 = +€9.419.000

Kumulierter ROI nach 5 Jahren: +€9.419.000
Break-Even: Monat 14
ROI-Ratio: 15:1

{
  "@context": "<https://schema.org>",
  "@type": ["TechArticle", "HowTo"],
  "headline": "Revolution durch Azure AI: Verfügbarkeit von GPT-5 und neue KI-Agenten-Plattformen",
  "description": "Umfassender Guide für CIOs zur Implementation von GPT-5 und Azure AI Foundry mit DSGVO-Compliance, ROI-Analyse und praktischen Implementierungsbeispielen.",
  "author": {
    "@type": "Person",
    "name": "Azure Solutions Architect",
    "description": "Spezialist für Enterprise Azure AI Implementierungen im DACH-Markt"
  },
  "publisher": {
    "@type": "Organization", 
    "name": "Enterprise Cloud Solutions"
  },
  "datePublished": "2024-01-15",
  "dateModified": "2024-01-15",
  "image": "<https://example.com/azure-ai-foundry-architecture.jpg>",
  "mainEntityOfPage": "<https://example.com/azure-ai-gpt5-enterprise-guide>",
  "inLanguage": "de-DE",
  "keywords": ["Azure AI", "GPT-5", "KI-Agenten", "Enterprise AI", "DSGVO", "Model Router"],
  "about": [
    {
      "@type": "Thing",
      "name": "Azure AI Foundry",
      "description": "Enterprise-Plattform für KI-Entwicklung und -Betrieb"
    },
    {
      "@type": "Thing", 
      "name": "GPT-5",
      "description": "Neueste Generation von OpenAI Large Language Models"
    }
  ],
  "step": [
    {
      "@typ

e": "HowToStep",
      "name": "Assessment und Vorbereitung",
      "text": "Azure-Abonnement konfigurieren und Compliance-Anforderungen analysieren"
    },
    {
      "@type": "HowToStep",
      "name": "Pilotimplementierung", 
      "text": "AI Foundry Workspace deployment und erste Agenten-Entwicklung"
    },
    {
      "@type": "HowToStep",
      "name": "Enterprise Rollout",
      "text": "Skalierung, Performance-Optimierung und Governance-Implementierung"
    }
  ],
  "totalTime": "PT6M",
  "estimatedCost": {
    "@type": "MonetaryAmount",
    "currency": "EUR",
    "value": "333000"
  },
  "supply": [
    "Azure Subscription",
    "Enterprise Admin Rights", 
    "GDPR Compliance Documentation"
  ],
  "tool": [
    "Azure CLI",
    "PowerShell",
    "Bicep Templates",
    "Python SDK"
  ]
}

{
  "@context": "<https://schema.org>",
  "@type": "FAQPage", 
  "mainEntity": [
    {
      "@type": "Question",
      "name": "Wie lange dauert eine vollständige Azure AI Foundry Implementation?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Eine typische Enterprise-Implementation dauert 6-9 Monate: 2 Monate Vorbereitung und Assessment, 3-4 Monate Pilotphase und 2-3 Monate für den produktiven Rollout."
      }
    },
    {
      "@type": "Question", 
      "name": "Welche DSGVO-Garantien bietet Microsoft für GPT-5?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Microsoft bietet Standard-Datenverarbeitungsverträge (DPAs) für alle Azure AI Services. GPT-5 wird in EU-Regionen gehostet, unterstützt Datenresidenz und bietet Löschungsgarantien für Kundendaten."
      }
    }
  ]
}