fix(ms-ai-architect): Foundry URL-navnerom-migrering (ai-foundry → foundry/foundry-classic, 141 filer)

Task #5 del 1/3 (URL-migrering). Verifiseringen motbeviste STATE.md-premisset om ren prefix-swap: rebrand er per-URL, ikke mekanisk. En blind sed ai-foundry→foundry ville lagd 56 nye 404-er (classic-stiene finnes ikke under nytt foundry/-prefiks — bekreftet empirisk).

Metode: resolverte alle 237 unike KB-URLer mot live redirects (curl -L), bygde full-URL→full-URL-mapping fra faktisk url_effective. Bevarer locale-form, query (?view=) og #fragment per lenke.

- 231 navnerom-erstatninger over 141 filer (408 forekomster):
  - 161 → azure/foundry/ (98 ren prefix-swap + 10 sti-reorg + reorg-tilfeller)
  - 69 → azure/foundry-classic/ (eldre hub-spor: assistants, hub-DR, on-your-data; faktisk redirect-mål per operatorvalg)
  - 1 → azure/foundry-local/

- 2 døde lenker (404) fikset til verifiserte mål:
  - agent-service → azure/foundry/agents/overview
  - concepts/evaluation-evaluators/ → azure/foundry/how-to/evaluate-generative-ai-app

- 5 path-/display-referanser (uten https://, i backticks/lenketekst) rettet manuelt.

- 6 slug-baserte ai-foundry-treff urørt (scope-grense): managed-grafana-dashboard, security-baseline, power-platform prompt-builder, architecture baseline-chat (sistnevnte slug-rebrand i annet navnerom — mulig fremtidig funn).

- Parkert til task #5 del 2/3: Norway East GPT-5-datasuverenitet-fiks + modellkatalog-utvidelse (5.3/5.4/5.5, gpt-oss, sora-2).

Verifisert: 0 gjenværende azure/ai-foundry/-navnerom i skills/. validate-plugin.sh 219 PASS. test-kb-integrity.sh 117/117 passed.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

Claude-Session: https://claude.ai/code/session_01REiKFhP4w6xGXXqWKpPCJJ

skills/ms-ai-governance/references/responsible-ai/continuous-improvement-feedback-loops.md

@@ -48,7 +48,7 @@ Microsoft implementerer feedback loops gjennom hele AI-livssyklusen – fra utvi
 - Error logs og exception traces
 - User feedback (thumbs up/down, ratings)
 
-**Confidence:** Verified – [MLflow Tracing](https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/tracing/), [Azure Monitor](https://learn.microsoft.com/en-us/azure/ai-foundry/concepts/observability)
+**Confidence:** Verified – [MLflow Tracing](https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/tracing/), [Azure Monitor](https://learn.microsoft.com/en-us/azure/foundry/concepts/observability)
 
 ### 2. Automated Quality Monitoring
 
@@ -69,7 +69,7 @@ Microsoft bruker automated scorers (LLM judges) for kontinuerlig kvalitetsvurder
 - Automated alerts ved threshold violations
 - Integration med Azure AI Foundry evaluation tools
 
-**Confidence:** Verified – [Generation Quality Monitoring](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/monitor-quality-safety?view=foundry-classic)
+**Confidence:** Verified – [Generation Quality Monitoring](https://learn.microsoft.com/en-us/azure/foundry-classic/how-to/monitor-quality-safety?view=foundry-classic)
 
 ### 3. Human Feedback Integration
 
@@ -258,7 +258,7 @@ model_monitor = MonitorSchedule(
 )
 ```
 
-**Confidence:** Verified – [Azure AI Foundry Monitoring](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/monitor-quality-safety?view=foundry-classic)
+**Confidence:** Verified – [Azure AI Foundry Monitoring](https://learn.microsoft.com/en-us/azure/foundry-classic/how-to/monitor-quality-safety?view=foundry-classic)
 
 ### MLflow on Azure Databricks
 
@@ -531,7 +531,7 @@ Models, prompts, eval datasets, scorers – full reproducibility er non-negotiab
    - Key content: 10-step feedback loop, human-aligned metrics, production monitoring
 
 2. **Azure AI Foundry Production Monitoring**
-   - URL: https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/monitor-quality-safety?view=foundry-classic
+   - URL: https://learn.microsoft.com/en-us/azure/foundry-classic/how-to/monitor-quality-safety?view=foundry-classic
    - Key content: Continuous evaluation, scorers, threshold configuration
 
 3. **AI Builder Feedback Loop**
@@ -571,7 +571,7 @@ Models, prompts, eval datasets, scorers – full reproducibility er non-negotiab
     - Key content: Feedback mechanisms, bias monitoring, iterative updates
 
 12. **Azure AI Foundry Observability Concepts**
-    - URL: https://learn.microsoft.com/en-us/azure/ai-foundry/concepts/observability
+    - URL: https://learn.microsoft.com/en-us/azure/foundry/concepts/observability
     - Key content: Tracing, monitoring features, model performance tracking
 
 **Code samples (Verified):**