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/genaiops-llm-specific-practices.md

@@ -1,7 +1,7 @@
 # GenAIOps - LLM-Specific MLOps Practices
 
 **Dato:** 2026-02-04
-**Last updated:** 2026-04 | Verified: MCP 2026-04
+**Last updated:** 2026-04
 **Kategori:** MLOps & GenAIOps
 **Konfidensgrad:** Høy (basert på 18 MCP-kilder fra Microsoft Learn)
 
@@ -180,17 +180,25 @@ MLflow Tracing provides end-to-end observability for GenAI applications:
 
 ### API Management som LLM Gateway
 
-**Hva:** Centralized gateway foran Azure OpenAI og eksterne LLM APIs.
+**Hva:** Centralized gateway foran Azure OpenAI in Foundry Models og andre LLM APIs.
 
 **GenAIOps use cases:**
-- **Load balancing**: Distribuer trafikk over multiple Azure OpenAI instances
-- **Throttling**: Rate limiting per user/subscription
-- **Token tracking**: Centralized logging av token consumption
-- **Cost allocation**: Chargeback til teams basert på usage
-- **A/B testing**: Route 10% traffic til ny modell, 90% til gammel
+- **Load balancing**: Distribuer trafikk over multiple Azure OpenAI instances (med health endpoint monitoring og circuit breaking)
+- **Throttling**: Rate limiting per user/subscription (token-per-minute og requests-per-minute)
+- **Token tracking**: Centralized logging av token consumption (cross-model observability)
+- **Cost allocation**: Chargeback til teams basert på usage (showback/chargeback for multitenant)
+- **A/B testing / Safe deployment**: Route 10% traffic til ny modell, 90% til gammel
 - **Circuit breaker**: Failover til backup LLM provider (OpenAI → Mistral)
+- **Federated authentication**: Extend client auth beyond Entra ID and API keys
+- **Data sovereignty**: Enforce regional routing compliance for GDPR
 
-**Konfidensgrad:** 90% — API Management for LLM er dokumentert pattern (2025).
+**Implementasjonsalternativer (Verified MCP 2026-04):**
+1. **Azure API Management** (anbefalt) — PaaS, built-in Azure OpenAI policies (`Limit Azure OpenAI API token usage`, `Emit metrics for consumption`), zone-redundant, multi-region. Bruk [GenAI gateway toolkit](https://github.com/Azure-Samples/apim-genai-gateway-toolkit) for custom policies + load-testing.
+2. **Custom code** — Deploy gateway-logikk til App Service, Container Apps eller AKS. Kan frontes av API Management for HTTP-gateway capabilities.
+
+**Viktig:** Global og data zone deployments i Azure OpenAI (som distribuerer kapasitet på tvers av datasentre) er i seg selv en gateway-implementasjon — vurder om disse dekker behovet FØR du legger til ekstra gateway-lag.
+
+**Konfidensgrad:** 90% — API Management for LLM er dokumentert pattern (Verified MCP 2026-04).
 
 ---
 
@@ -347,7 +355,7 @@ MLflow Tracing provides end-to-end observability for GenAI applications:
 13. [GenAI app developer workflow](https://learn.microsoft.com/en-us/azure/databricks/generative-ai/tutorials/ai-cookbook/genai-developer-workflow)
 14. [Plan and prepare a GenAIOps solution (Microsoft Learn Training)](https://learn.microsoft.com/en-us/training/modules/plan-prepare-genaiops/)
 15. [Implement LLMOps in Azure Databricks (Microsoft Learn Training)](https://learn.microsoft.com/en-us/training/modules/implement-llmops-azure-databricks/)
-16. [Azure OpenAI Gateway Guide](https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/azure-openai-gateway-guide)
+16. [Access Azure OpenAI in Foundry Models through a gateway](https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/azure-openai-gateway-guide) (Verified MCP 2026-04)
 17. [RAG solution design and evaluation guide](https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/rag/rag-solution-design-and-evaluation-guide)
 18. [Microsoft GenAIOps Prompt Flow Template (GitHub)](https://github.com/microsoft/genaiops-promptflow-template)