docs(architect): weekly KB update — 52 files refreshed (2026-04)

Key content changes:
- MLOps: MLflow 3 scorers expanded (RetrievalRelevance, Fluency, multi-turn judges)
- MLflow 3 A/B eval: mirror_traffic GA confirmed, new scorer catalog
- CI/CD: OIDC auth replaces deprecated --sdk-auth (Azure ML GitHub Actions)
- Agent framework A2A: updated SDK patterns (A2ACardResolver, BearerAuth)
- AG-UI backend tool rendering: accurate TOOL_CALL_* event shapes
- Computer Use agents: US region requirement, credentials patterns
- Purview governance: bulk term edit, expire/delete workflows
- CAF AI Secure: 3-phase structure confirmed current
- Copilot Studio: Claude Sonnet 4.5/4.6 GA, new orchestration controls
- M365 manifest: v1.26 GA (April 2026), copilotAgents node
- Power Platform: agent flow capacity enforcement corrected
- Azure Monitor: Simple Log Alerts GA, AMBA for policy-based alerting
- Security Copilot: SCU capacity model (400 SCU/1000 users)
- EU Data Boundary: all EU + EFTA countries confirmed
- gateway-multi-backend: added 4th topology, subscription-level quota note

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

skills/ms-ai-engineering/references/mlops-genaiops/model-deployment-strategies-azure.md

@@ -5,7 +5,7 @@
 **Målgruppe:** Arkitekter som planlegger ML-modellutplassering i produksjon
 **Konfidensgrad:** ⚡️⚡️⚡️ Høy (basert på Microsoft Learn + offisielle code samples)
 
-**Verified:** MCP 2026-04
+**Last updated:** 2026-04
 
 ## Introduksjon
 
@@ -620,6 +620,8 @@ deployment = ManagedOnlineDeployment(
 - Built-in support for scikit-learn, TensorFlow, PyTorch
 - Enklere rollback (bare endre model version)
 
+**Auth note (Verified MCP 2026-04):** For production deployments, use Microsoft Entra token-based authentication (`aad_token`) instead of key-based auth — provides identity-based access control.
+
 **Referanse:** [Deploy MLflow models to online endpoints](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-mlflow-models-online-endpoints?view=azureml-api-2)
 
 ---
@@ -1020,7 +1022,7 @@ Denne kunnskapsreferansen er basert på følgende Microsoft Learn-artikler og co
 
 **Primære kilder:**
 1. [Perform safe rollout of new deployments for real-time inference](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-safely-rollout-online-endpoints?view=azureml-api-2)
-   → Komplett guide til blue-green deployment og traffic mirroring
+   → Komplett guide til blue-green deployment og traffic mirroring (Verified MCP 2026-04)
 
 2. [MLOps model management with Azure Machine Learning](https://learn.microsoft.com/en-us/azure/machine-learning/concept-model-management-and-deployment?view=azureml-api-2)
    → Oversikt over deployment capabilities og controlled rollout
@@ -1032,7 +1034,7 @@ Denne kunnskapsreferansen er basert på følgende Microsoft Learn-artikler og co
    → Canary deployment med Azure DevOps Pipelines
 
 5. [Progressive rollout of MLflow models to Online Endpoints](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-mlflow-models-online-progressive?view=azureml-api-2)
-   → MLflow-spesifikk progressive rollout
+   → MLflow-spesifikk progressive rollout; supports model packaging (--with-package) for endpoints without egress connectivity (Verified MCP 2026-04)
 
 **Code samples:**
 - [azureml-examples/sdk/python/endpoints/online/managed/online-endpoints-safe-rollout.ipynb](https://github.com/Azure/azureml-examples/blob/main/sdk/python/endpoints/online/managed/online-endpoints-safe-rollout.ipynb)
@@ -1040,7 +1042,7 @@ Denne kunnskapsreferansen er basert på følgende Microsoft Learn-artikler og co
 
 **Well-Architected Framework:**
 - [Architecture strategies for safe deployment practices](https://learn.microsoft.com/en-us/azure/well-architected/operational-excellence/safe-deployments)
-   → Progressive exposure model, bake times, rollback strategies
+   → Progressive exposure model, bake times, rollback strategies (Verified MCP 2026-04 — adds safe decommissioning guidance + AI opportunity note for GenAI-assisted rollout tuning)
 
 **Pricing (sist verifisert: 2026-02-04):**
 - [Azure Machine Learning pricing](https://azure.microsoft.com/en-us/pricing/details/machine-learning/)
@@ -1050,8 +1052,8 @@ Denne kunnskapsreferansen er basert på følgende Microsoft Learn-artikler og co
 
 ---
 
-**Sist oppdatert:** 2026-02-04
-**Neste review:** 2026-05-04 (eller ved større endringer i Azure ML deployment capabilities)
+**Sist oppdatert:** 2026-04-10
+**Neste review:** 2026-07-10 (eller ved større endringer i Azure ML deployment capabilities)
 
 
 ### Safe Rollout / Blue-Green Deployment (Azure Well-Architected 2026)
@@ -1085,3 +1087,7 @@ az ml online-endpoint update --name my-endpoint --traffic blue=90 green=10
 
 **Emergency SDP**: Prescriptive protocols for hotfix acceleration — approval stage and bake time reduction — with explicit approval criteria.
 
+**Safe decommissioning (new in 2026-04)**: Removing components is highest-risk. Steps: validate inactivity → preserve state (backup/export) → disable before deleting → monitor watch window covering full usage cycle → clean up residual references. Skip disable only if compliance requires immediate removal.
+
+**AI opportunity**: AI can assist rollout tuning — analyze deployment docs, code reviews, incident history to suggest rollout strategies and parameters (low/medium GenAI approach). Advanced agentic solutions can auto-update rollout configurations.
+