docs(ms-ai-architect): KB-refresh tema-b — Foundry-navnesveip «Azure AI Foundry»→«Microsoft Foundry» (233 filer)

Verifisert mot offisiell MS-doc (juni 2026): «Microsoft Foundry» er det
gjeldende produkt-/portalnavnet; «Foundry (classic)» = gamle «Azure AI Foundry»
(/azure/foundry/ vs /azure/foundry-classic/). Premiss bekreftet før sveip.

Multi-regel, IKKE naiv s/Azure AI Foundry/Microsoft Foundry/ — MS dropper
«Azure AI» (legger IKKE til «Microsoft») for to produktvarianter:
- «Azure AI Foundry Agent[ Service|s]» → «Foundry Agent Service/Agents» (MS-form)
- «Azure AI Foundry Models» → «Foundry Models» (i «Azure OpenAI in Foundry Models»)
- «Azure AI Foundry SDK» → «Microsoft Foundry SDK» (operatør-valg)
- «Azure AI Foundry portal/project» + generisk → «Microsoft Foundry»
- Pre-eksisterende «Microsoft Foundry Models» (4) normalisert → «Foundry Models»

Bevart: «Azure OpenAI», «Azure AI Inference SDK», «Azure AI Search»,
«Azure AI Services», kode-IDer. Historisk ref «(tidligere Azure AI Foundry)»
i model-catalog-2026.md beskyttet via lookbehind. URL /azure/ai-foundry/→
/azure/foundry/ kun i owasp-llm-top10 (KB-ref); docs/-filer deferred.

Scope: skills (inkl. 3 SKILL.md) + commands + agents + README + CLAUDE.
Ekskludert: docs/ (interne), playground/+tests/ fixtures (testdata),
CHANGELOG.md (historisk logg), STATE.md (gitignored).

3 SKILL.md endret (advisor/engineering/security) → judge-cache teknisk
invalidert for disse, men scorer uendret: advisor 91, eng/gov/infra/sec 96
(alle ≥90). validate 239/0. 0 «Azure AI Foundry» igjen (utenom bevart ref).

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

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

@@ -67,7 +67,7 @@ Microsoft bruker automated scorers (LLM judges) for kontinuerlig kvalitetsvurder
 - Schedulert evaluering (f.eks. daglig via CronTrigger)
 - Real-time scoring av sampled production traffic
 - Automated alerts ved threshold violations
-- Integration med Azure AI Foundry evaluation tools
+- Integration med Microsoft Foundry evaluation tools
 
 **Confidence:** Verified – [Generation Quality Monitoring](https://learn.microsoft.com/en-us/azure/foundry-classic/how-to/monitor-quality-safety?view=foundry-classic)
 
@@ -144,7 +144,7 @@ Benchmark datasets med kjent kvalitet for consistent testing og model validation
 
 **Verktøy:**
 - Azure Databricks MLflow 3
-- Azure AI Foundry Agent Service
+- Foundry Agent Service
 - MLflow Tracing & Scorers
 
 **Confidence:** Verified – [MLflow Continuous Improvement](https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/overview/)
@@ -223,7 +223,7 @@ Alle nye services instrumenteres for monitoring/logging fra dag 1, slik at feedb
 
 ## Integrasjon med Microsoft-stakken
 
-### Azure AI Foundry
+### Microsoft Foundry
 
 **Production monitoring:**
 - **Continuous evaluation**: Scheduled scoring av production traces
@@ -258,7 +258,7 @@ model_monitor = MonitorSchedule(
 )
 ```
 
-**Confidence:** Verified – [Azure AI Foundry Monitoring](https://learn.microsoft.com/en-us/azure/foundry-classic/how-to/monitor-quality-safety?view=foundry-classic)
+**Confidence:** Verified – [Microsoft Foundry Monitoring](https://learn.microsoft.com/en-us/azure/foundry-classic/how-to/monitor-quality-safety?view=foundry-classic)
 
 ### MLflow on Azure Databricks
 
@@ -440,7 +440,7 @@ PDF-rapport for sharing med stakeholders (technical + non-technical), dokumenter
 
 ### Lisensiering
 
-**Azure AI Foundry:**
+**Microsoft Foundry:**
 - Pay-as-you-go for monitoring, evaluation, storage
 - Serverless Spark compute for monitoring schedules
 
@@ -532,7 +532,7 @@ Models, prompts, eval datasets, scorers – full reproducibility er non-negotiab
    - URL: https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/overview/
    - Key content: 10-step feedback loop, human-aligned metrics, production monitoring
 
-2. **Azure AI Foundry Production Monitoring**
+2. **Microsoft Foundry Production Monitoring**
    - 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
 
@@ -572,7 +572,7 @@ Models, prompts, eval datasets, scorers – full reproducibility er non-negotiab
     - URL: https://learn.microsoft.com/en-us/microsoft-copilot-studio/guidance/responsible-ai
     - Key content: Feedback mechanisms, bias monitoring, iterative updates
 
-12. **Azure AI Foundry Observability Concepts**
+12. **Microsoft Foundry Observability Concepts**
     - URL: https://learn.microsoft.com/en-us/azure/foundry/concepts/observability
     - Key content: Tracing, monitoring features, model performance tracking