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/mlops-teams-collaboration-tools.md

@@ -1,7 +1,7 @@
 # MLOps Team Collaboration and Tools Integration
 
 **Kategori:** MLOps & GenAIOps
-**Sist oppdatert:** 2026-02-04
+**Sist oppdatert:** 2026-04
 **Kilde:** Microsoft Learn, Azure Architecture Center
 **Konfidensgradering:** ⭐⭐⭐⭐⭐ (Verifisert mot offisiell Microsoft-dokumentasjon)
 
@@ -146,10 +146,12 @@ Azure DevOps provides end-to-end project management for ML teams:
 - `azure/login@v2` + `az ml job create` pattern
 - MLOps v2 solution accelerator: `Azure/mlops-v2-gha-demo`
 
-**Databricks CI/CD best practices**:
-- Feature branching with short-lived branches
-- Automated notebook testing before merge
+**Databricks CI/CD best practices (Verified MCP 2026-04)**:
+- Feature branching with short-lived branches (Gitflow aligned with dev/staging/prod environments)
+- Automated notebook testing before merge (bundle validate + pytest/ScalaTest)
 - MLflow experiment tracking integrated into PR workflows
+- **Declarative Automation Bundles** (formerly Databricks Asset Bundles) recommended for unified code+infra deployment
+- Workload identity federation (eliminates Databricks secrets) recommended for CI/CD auth
 
 **Formål:** CI/CD automation for ML lifecycle
 **Nøkkelkapabiliteter:**
@@ -675,13 +677,13 @@ Databricks MLOps Stacks demonstrerer best practice for multi-team collaboration:
 
 3. **What is Azure DevOps?**
    URL: https://learn.microsoft.com/en-us/azure/devops/user-guide/what-is-azure-devops
-   Hentet: 2026-02-04
-   Relevans: Azure Boards capabilities, team collaboration features
+   Hentet: 2026-04-10
+   Relevans: Azure Boards capabilities, team collaboration features (Verified MCP 2026-04 — new: Azure DevOps MCP Server for natural language project management queries, AI-Enhanced management with Copilot integration)
 
 4. **Best Practices and Recommended CI/CD Workflows on Databricks**
    URL: https://learn.microsoft.com/en-us/azure/databricks/dev-tools/ci-cd/best-practices
-   Hentet: 2026-02-04
-   Relevans: MLOps Stacks team collaboration table
+   Hentet: 2026-04-10
+   Relevans: MLOps Stacks team collaboration table (Verified MCP 2026-04 — now covers Declarative Automation Bundles, workload identity federation for auth, SQL and dashboard CI/CD workflows)
 
 5. **Set up MLOps with Azure DevOps**
    URL: https://learn.microsoft.com/en-us/azure/machine-learning/how-to-setup-mlops-azureml
@@ -690,8 +692,8 @@ Databricks MLOps Stacks demonstrerer best practice for multi-team collaboration:
 
 6. **Use GitHub Actions with Azure Machine Learning**
    URL: https://learn.microsoft.com/en-us/azure/machine-learning/how-to-github-actions-machine-learning
-   Hentet: 2026-02-04
-   Relevans: GitHub Actions integration patterns
+   Hentet: 2026-04-10
+   Relevans: GitHub Actions integration patterns (Verified MCP 2026-04 — OIDC recommended with Entra app or user-assigned managed identity)
 
 7. **MLOps Workflows on Azure Databricks**
    URL: https://learn.microsoft.com/en-us/azure/databricks/machine-learning/mlops/mlops-workflow