docs(architect): weekly KB update — 106 files refreshed (2026-04)

Updates across all 5 skills: ms-ai-advisor, ms-ai-engineering,
ms-ai-governance, ms-ai-security, ms-ai-infrastructure.

Key changes:
- Language Services (Custom Text Classification, Text Analytics, QnA):
  retirement warning 2029-03-31, migration guides to Foundry/GPT-4o
- Agentic Retrieval: 50M free reasoning tokens/month (Public Preview)
- Computer Use: Claude Sonnet 4.5 (preview) + OpenAI CUA models
- Agent Registry: Risks column (M365 E7), user-shared/org-published types
- Declarative agents: schema v1.5 → v1.6, Store validation requirements
- MLflow 3: 13 built-in LLM judges, production monitoring, Genie Code
- AG-UI HITL: ApprovalRequiredAIFunction (C#) + @tool(approval_mode) (Python)
- Entra ID Ignite 2025: Agent ID Admin/Developer RBAC roles, Conditional Access
- Security Copilot: 400 SCU/month per 1000 M365 E5 licenses, auto-provisioned
- Fast Transcription API: phrase lists, 14-language multi-lingual transcription
- Azure Monitor Workbooks: Bicep support, RBAC specifics
- Power Platform Copilot: data residency (Norway/Europe → EU DB, Bing → USA)
- RAG security-rbac: 4-approach table (GA + 3 preview access control methods)
- IaC MLOps: Well-Architected OE:05 principles, Bicep/Terraform patterns
- Translator: image file batch translation Preview (JPEG/PNG/BMP/WebP)

All 106 files: Last updated 2026-04 | Verified: MCP 2026-04

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

skills/ms-ai-security/references/performance-scalability/async-processing-patterns.md

@@ -1,6 +1,6 @@
 # Asynchronous Processing Patterns
 
-**Last updated:** 2026-02
+**Last updated:** 2026-04 | Verified: MCP 2026-04
 **Status:** GA
 **Category:** Performance & Scalability
 
@@ -428,6 +428,84 @@ public class AIRequestController : ControllerBase
 }
 ```
 
+
+## Event-Driven Architecture Styles (oppdatert 2026-04)
+
+Microsoft dokumenterer to primære topologier for event-drevet AI-prosessering:
+
+### Broker-topologi vs. Mediator-topologi
+
+| Aspekt | Broker-topologi | Mediator-topologi |
+|--------|----------------|-------------------|
+| Koordinering | Events publiseres direkte til broker | Central mediator koordinerer workflow |
+| Eksempel | Azure Event Hubs + Service Bus | Azure Durable Functions |
+| Kobling | Løs kobling mellom produsenter/konsumenter | Sterkere kobling via mediator |
+| Bruksscenario | Høyvolum streaming, uavhengige konsumenter | Komplekse AI-arbeidsflyter med avhengigheter |
+
+### Azure Event Hubs vs. Azure Event Grid
+
+| Service | Type | Bruksscenario |
+|---------|------|---------------|
+| **Azure Event Hubs** | Durable event stream (log) | AI-inferensresultater som skal prosesseres av mange konsumenter |
+| **Azure Event Grid** | Publish-subscribe, reaktiv | Trigger AI-jobb ved filnedlasting, blob-endring |
+| **Azure Service Bus** | Message queue, garantert levering | Jobb-kø for AI-prosessering med retry og dead-letter |
+
+### Utfordringer i event-drevne AI-arkitekturer
+
+```python
+# Utfordring 1: Garantert levering
+# Bruk Service Bus med peek-lock for å garantere at AI-jobb fullføres
+
+from azure.servicebus import ServiceBusClient, ServiceBusMessage
+import json
+
+def process_ai_job_safely(
+    servicebus_conn: str,
+    queue_name: str,
+    ai_processor
+) -> None:
+    """Garantert levering via peek-lock mønster."""
+    with ServiceBusClient.from_connection_string(servicebus_conn) as sb:
+        with sb.get_queue_receiver(queue_name, max_wait_time=5) as receiver:
+            for message in receiver:
+                # Peek-lock: meldingen er reservert, ikke slettet
+                try:
+                    payload = json.loads(str(message))
+                    result = ai_processor(payload)
+                    # Fullfør melding (slett fra kø) kun ved suksess
+                    receiver.complete_message(message)
+                    publish_result(result)
+                except Exception as e:
+                    # Abandon: meldingen returneres til kø for ny levering
+                    receiver.abandon_message(message)
+
+# Utfordring 2: Eventual consistency
+# AI-resultater publiseres asynkront — bruk correlation ID for sporing
+
+def create_ai_job(correlation_id: str, payload: dict) -> dict:
+    """Returner job receipt umiddelbart, resultat kommer asynkront."""
+    return {
+        "correlation_id": correlation_id,
+        "status": "accepted",
+        "result_url": f"/api/results/{correlation_id}",
+        "estimated_completion_seconds": 30
+    }
+
+# Utfordring 3: Ordregaranti
+# Event Hubs garanterer ordre innen én partisjon
+# Bruk samme partisjonsnøkkel for relaterte AI-forespørsler
+
+def publish_ordered_event(
+    producer,
+    partition_key: str,  # f.eks. dokument-ID
+    event_data: dict
+) -> None:
+    from azure.eventhub import EventData
+    event = EventData(json.dumps(event_data))
+    event.properties = {"partition_key": partition_key}
+    producer.send_batch([event], partition_key=partition_key)
+```
+
 ## Norsk offentlig sektor
 
 - **Saksbehandlingssystemer**: Asynkron prosessering er ideelt for AI-assistert saksbehandling der analyse kan ta tid. Saksbehandler sender inn dokument, fortsetter med annet arbeid, og mottar notifikasjon når analysen er ferdig.