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-engineering/references/rag-architecture/agentic-rag-patterns.md

@@ -1,6 +1,6 @@
 # Agentic RAG Patterns — Agent-styrt retrieval
 
-**Last updated:** 2026-02
+**Last updated:** 2026-04 | Verified: MCP 2026-04
 **Status:** GA (Semantic Kernel), Preview (Azure AI Search agentic retrieval)
 **Category:** RAG Architecture & Semantic Search
 
@@ -285,3 +285,24 @@ agent = chat_client.as_agent(
 | AI Agent Design Patterns | **Verified** | [learn.microsoft.com](https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/ai-agent-design-patterns) |
 | Semantic Kernel Agent Orchestration | **Verified** | [learn.microsoft.com](https://learn.microsoft.com/en-us/semantic-kernel/frameworks/agent/agent-orchestration/) |
 | Multi-agent performance (34% accuracy) | **Baseline** | Community source (ragaboutit.com) |
+
+
+### Azure AI Search Agentic Retrieval (Public Preview — oppdatert 2026-04)
+
+Azure AI Search agentic retrieval er en managed multi-query pipeline for komplekse spørsmål i chat og copilot-apper:
+
+**Funksjonalitet:**
+- LLM (gpt-4o/4.1/5-serien) bryter ned komplekse queries til fokuserte subqueries
+- Subqueries kjøres **parallelt** med semantisk reranking per query
+- Resultater slås sammen til ett grounding data-sett med query plan og source documents
+- Leser inn chat history for kontekstuell query planning
+
+**Prising:**
+- Free plan: **50 millioner gratis reasoning tokens/mnd** (alle tiers)
+- Standard plan: pay-as-you-go etter fri kvote
+- Avhenger av semantic ranker (premium feature)
+
+**Arkitektur:** Knowledge Base + Knowledge Source(s) + Azure OpenAI LLM + Azure AI Search index
+
+**AI Agent Design Patterns (Azure Architecture Center):**
+Agentic RAG plasseres i et spektrum fra single model call → single agent with tools → multi-agent orchestration. Start med laveste nødvendige kompleksitetsnivå. Mønstre: sequential (pipeline), parallel fanout, supervisor, og autonomous loop. Multi-agent krever koordineringsoverhead og økt latency — bruk kun når single-agent RAG ikke er tilstrekkelig.