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/mlops-genaiops/llm-evaluation-production.md

@@ -6,6 +6,8 @@
 
 ---
 
+**Verified:** MCP 2026-04
+
 ## Introduksjon
 
 LLM-evaluering i produksjonsmiljø er fundamentalt forskjellig fra tradisjonell ML-evaluering. Mens klassiske ML-modeller evalueres med deterministiske metrikker på statiske test-sett, krever generative AI-applikasjoner kontinuerlig evaluering av åpne, ikke-deterministiske output i dynamiske produksjonsscenarioer.
@@ -558,6 +560,38 @@ project = AIProjectClient.from_connection_string(
 
 ### MLflow 3 + Databricks Unity Catalog
 
+
+### MLflow 3 LLM Evaluation Framework (2026)
+
+MLflow 3 (SDK `mlflow[databricks]>=3.1`) introduces a unified evaluation model:
+
+**Core architecture**: Traces → Scorers → Feedback
+- Traces from `mlflow.genai.evaluate()` or production monitoring service
+- Scorers parse traces, assess quality, return `Feedback` objects
+- Same scorers used in development AND production (consistent lifecycle)
+
+**Built-in LLM judges** (research-validated):
+
+| Judge | Needs Ground Truth | Evaluates |
+|-------|-------------------|-----------|
+| `RelevanceToQuery` | No | Response relevance to user request |
+| `RetrievalGroundedness` | No | Hallucination detection |
+| `Safety` | No | Harmful/toxic content |
+| `Correctness` | Yes | Accuracy vs ground truth |
+| `Completeness` | Yes | All questions addressed |
+| `ToolCallCorrectness` | Yes | Tool calls and arguments |
+| `ToolCallEfficiency` | No | Redundant tool usage |
+| `Guidelines` | No | Custom natural-language rules |
+
+**Multi-turn judges** (conversation-level): `ConversationCompleteness`, `UserFrustration`, `KnowledgeRetention`, `ConversationalSafety`
+
+**Production monitoring**: Automatically runs scorers on production traces; uses Databricks-hosted LLM judges (EU workspaces: EU-hosted models). No prompts stored with Azure OpenAI (Abuse Monitoring opt-out).
+
+**Custom judges**: Full control over evaluation criteria, scores (numerical/categorical/boolean), human feedback alignment via `align_judges()`.
+
+**Key note**: MLflow 3 replaced Agent Evaluation SDK — migrate with `mlflow.genai.*` functions.
+
+
 **Enterprise governance for AI:**
 
 ```