from agentic_fleet.integrations.azure import ServiceBusQueue

queue = ServiceBusQueue(
    connection_string="your-connection-string",
    queue_name="fleet-tasks"
)

# Send message
await queue.send_message({
    "type": "code_review",
    "content": "Review PR #123"
})

# Receive message
@queue.message_handler("fleet-tasks")
async def handle_task(message):
    task_type = message.body["type"]
    await fleet.process_task(task_type)

from agentic_fleet.integrations.azure import ServiceBusTopic

topic = ServiceBusTopic(
    connection_string="your-connection-string",
    topic_name="fleet-events"
)

# Publish event
await topic.publish_event({
    "event": "code_generated",
    "fleet_id": "fleet_123"
})

# Subscribe to events
@topic.subscription_handler("code-events")
async def handle_code_event(event):
    if event.body["event"] == "code_generated":
        await process_code(event.body)

from agentic_fleet.integrations.azure import EventGrid

event_grid = EventGrid(
    topic_endpoint="your-topic-endpoint",
    access_key="your-access-key"
)

# Publish custom event
await event_grid.publish_event(
    event_type="FleetEvent",
    subject="fleet/status-change",
    data={
        "fleet_id": "fleet_123",
        "status": "active"
    }
)

# Event handler
@event_grid.event_handler
async def handle_fleet_event(event):
    if event.event_type == "FleetEvent":
        await update_fleet_status(event.data)

from agentic_fleet.integrations.azure import LanguageService

language = LanguageService(
    endpoint="your-endpoint",
    key="your-key"
)

# Analyze text
analysis = await language.analyze_text(
    text="Create a new API endpoint",
    features=["entities", "sentiment"]
)

# Custom task routing
intent = await language.detect_intent(
    text="Review this code for security issues"
)
await fleet.route_task(intent)

# azure-pipelines.yml
trigger:
  - main

pool:
  vmImage: 'ubuntu-latest'

variables:
  - group: fleet-variables

stages:
  - stage: Build
    jobs:
      - job: BuildAndTest
        steps:
          - task: UsePythonVersion@0
            inputs:
              versionSpec: '3.11'
          
          - script: |
              python -m pip install --upgrade pip
              pip install -r requirements.txt
            displayName: 'Install dependencies'
          
          - script: |
              pytest tests/
            displayName: 'Run tests'

  - stage: Deploy
    jobs:
      - job: DeployToAKS
        steps:
          - task: AzureCLI@2
            inputs:
              azureSubscription: 'Azure subscription'
              scriptType: 'bash'
              scriptLocation: 'inlineScript'
              inlineScript: |
                az aks get-credentials --resource-group $(resourceGroup) --name $(clusterName)
                kubectl apply -f k8s/

from agentic_fleet.integrations.azure import DevOpsClient

devops = DevOpsClient(
    organization="your-org",
    project="your-project",
    pat="your-pat"
)

# Create work item
work_item = await devops.create_work_item(
    type="Task",
    title="Review generated code",
    description="Code review needed for PR #123",
    tags=["fleet", "code-review"]
)

# Update work item
await devops.update_work_item(
    id=work_item.id,
    status="In Progress",
    comment="Code review in progress"
)

# api-management.yaml
apiVersion: apimanagement.azure.com/v1alpha1
kind: APIManagement
metadata:
  name: fleet-api
spec:
  location: eastus
  sku:
    name: Developer
    capacity: 1
  publisher:
    name: AgenticFleet
    email: [email protected]
  apis:
    - name: fleet-api
      displayName: Fleet Management API
      protocols:
        - https
      path: /v1
      format: openapi
      value: ./openapi.yaml

<policies>
    <inbound>
        <base />
        <rate-limit calls="100" renewal-period="60" />
        <validate-jwt header-name="Authorization" failed-validation-httpcode="401">
            <openid-config url="https://login.microsoftonline.com/common/.well-known/openid-configuration" />
        </validate-jwt>
    </inbound>
</policies>

{
  "frontDoor": {
    "name": "fleet-frontend",
    "routingRules": [
      {
        "name": "api-rule",
        "acceptedProtocols": ["Https"],
        "patternsToMatch": ["/api/*"],
        "backendPool": {
          "name": "fleet-backend",
          "backends": [
            {
              "address": "fleet-api.azurewebsites.net",
              "httpPort": 80,
              "httpsPort": 443,
              "priority": 1,
              "weight": 100
            }
          ]
        }
      }
    ]
  }
}

from agentic_fleet.integrations.azure import BackupManager

backup = BackupManager(
    storage_account="your-account",
    container="backups"
)

# Create backup
backup_id = await backup.create_backup(
    fleet_id="fleet_123",
    include_memory=True
)

# Restore from backup
await backup.restore_backup(
    backup_id=backup_id,
    target_fleet="fleet_124"
)

from agentic_fleet.integrations.azure import DisasterRecovery

dr = DisasterRecovery(
    primary_region="eastus",
    secondary_region="westus"
)

# Configure failover
await dr.configure_failover(
    fleet_id="fleet_123",
    mode="active-passive",
    auto_failover=True
)

# Manual failover
await dr.initiate_failover(
    fleet_id="fleet_123",
    target_region="westus"
)

from agentic_fleet.integrations.azure import CDNManager

cdn = CDNManager(
    profile_name="fleet-cdn",
    endpoint_name="fleet-api"
)

# Configure caching
await cdn.configure_caching(
    rules=[
        {
            "path": "/api/static/*",
            "duration": "1d"
        },
        {
            "path": "/api/data/*",
            "duration": "1h"
        }
    ]
)

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: fleet-autoscaler
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: agentic-fleet
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80

from agentic_fleet.integrations.azure import MetricsCollector

metrics = MetricsCollector(
    namespace="fleet-metrics"
)

# Track custom metrics
await metrics.track_metric(
    name="fleet_response_time",
    value=0.5,
    dimensions={
        "fleet_id": "fleet_123",
        "agent_id": "agent_456"
    }
)

# Create dashboard
await metrics.create_dashboard(
    name="Fleet Performance",
    metrics=[
        "fleet_response_time",
        "agent_memory_usage",
        "message_count"
    ]
)