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

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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
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Kjell Tore Guttormsen 2026-06-18 13:37:06 +02:00
commit dd1036ab8a
141 changed files with 399 additions and 399 deletions
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skills/ms-ai-engineering/references/mlops-genaiops/feedback-loops-continuous-improvement.md
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@ -715,7 +715,7 @@ mlflow.log_param("user_id_hash", user_id_hash) # Logged
1. [MLflow for GenAI Apps and Agents - Continuous Improvement Cycle](https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/overview/) (Verified MCP 2026-04 — updated 10-step cycle; new: Trace UI for pattern identification, evaluation harness, version/prompt management tracking)
2. [Machine Learning Operations v2 - Monitoring & Feedback](https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/machine-learning-operations-v2)
3. [Generative AI App Developer Workflow - Production Monitoring](https://learn.microsoft.com/en-us/azure/databricks/generative-ai/tutorials/ai-cookbook/genai-developer-workflow)
4. [Azure AI Foundry - Observability in Generative AI](https://learn.microsoft.com/en-us/azure/ai-foundry/concepts/observability)
4. [Azure AI Foundry - Observability in Generative AI](https://learn.microsoft.com/en-us/azure/foundry/concepts/observability)
5. [MLOps and GenAIOps for AI Workloads - Model Maintenance](https://learn.microsoft.com/en-us/azure/well-architected/ai/mlops-genaiops#model-maintenance)
6. [AI Builder - Continuously Improve Your Model (Feedback Loop)](https://learn.microsoft.com/en-us/ai-builder/feedback-loop)

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skills/ms-ai-engineering/references/mlops-genaiops/genaiops-llm-specific-practices.md
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@ -350,7 +350,7 @@ MLflow Tracing provides end-to-end observability for GenAI applications:
8. [Azure AI Evaluation SDK](https://learn.microsoft.com/en-us/python/api/overview/azure/ai-evaluation-readme)
9. [Mosaic AI capabilities for GenAI](https://learn.microsoft.com/en-us/azure/databricks/generative-ai/guide/mosaic-ai-gen-ai-capabilities)
10. [MLflow Prompt Registry](https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/prompt-version-mgmt/prompt-registry/)
11. [Azure AI Foundry monitoring](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/monitor-quality-safety)
11. [Azure AI Foundry monitoring](https://learn.microsoft.com/en-us/azure/foundry-classic/how-to/monitor-quality-safety)
12. [MLflow Tracing for GenAI](https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/tracing/)
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/)

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skills/ms-ai-engineering/references/mlops-genaiops/governance-audit-ml-operations.md
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@ -104,7 +104,7 @@ Azure Policy lar deg definere *guardrails* for hvilke modeller som kan deployes,
**Kilder:**
- [Audit and manage Azure Machine Learning with Azure Policy](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-integrate-azure-policy?view=azureml-api-2)
- [Azure AI Foundry built-in policies](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/azure-policy)
- [Azure AI Foundry built-in policies](https://learn.microsoft.com/en-us/azure/foundry-classic/how-to/azure-policy)
- [Govern Azure platform services (PaaS) for AI](https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/scenarios/ai/platform/governance)
---

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skills/ms-ai-engineering/references/mlops-genaiops/inferencing-optimization-caching.md
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@ -953,7 +953,7 @@ Diagnostikk:
*Verifisert: 2026-02-04* — Komplett guide til ONNX Runtime, model conversion, deployment
2. **Prompt Caching (Azure OpenAI)**
https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/prompt-caching?view=foundry-classic
https://learn.microsoft.com/en-us/azure/foundry/openai/how-to/prompt-caching?view=foundry-classic
*Verifisert: 2026-02-04* — Official docs for prompt caching, supported models, pricing
3. **Application Design for AI Workloads on Azure**

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skills/ms-ai-engineering/references/mlops-genaiops/llm-evaluation-production.md
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@ -1053,16 +1053,16 @@ Production evaluation er ikke komplett uten human review loop. Anbefal:
### Primærkilder (Official Microsoft Documentation)
1. **Azure AI Foundry Evaluation SDK:**
[Evaluate your generative AI application locally with the Azure AI Evaluation SDK](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/develop/evaluate-sdk) – Comprehensive guide til local og cloud evaluation
[Evaluate your generative AI application locally with the Azure AI Evaluation SDK](https://learn.microsoft.com/en-us/azure/foundry-classic/how-to/develop/evaluate-sdk) – Comprehensive guide til local og cloud evaluation
2. **Continuous Evaluation for Agents:**
[Continuously evaluate your AI agents (preview)](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/continuous-evaluation-agents) – Production monitoring architecture og SDK examples
[Continuously evaluate your AI agents (preview)](https://learn.microsoft.com/en-us/azure/foundry-classic/how-to/continuous-evaluation-agents) – Production monitoring architecture og SDK examples
3. **MLflow 3 Evaluation & Monitoring:**
[Evaluate and monitor AI agents - Azure Databricks](https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/eval-monitor/) – MLflow 3 evaluation harness og production scorers
4. **Observability Overview:**
[Observability in generative AI - Azure AI Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry/concepts/observability) – High-level GenAIOps lifecycle og evaluator taxonomy
[Observability in generative AI - Azure AI Foundry](https://learn.microsoft.com/en-us/azure/foundry/concepts/observability) – High-level GenAIOps lifecycle og evaluator taxonomy
5. **Model Monitoring for Generative AI:**
[Model monitoring for generative AI applications (preview)](https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-monitor-generative-ai-applications) – Azure ML Prompt Flow monitoring approach
@ -1071,7 +1071,7 @@ Production evaluation er ikke komplett uten human review loop. Anbefal:
[Azure AI Evaluation client library for Python](https://learn.microsoft.com/en-us/python/api/overview/azure/ai-evaluation-readme) – API docs for all built-in evaluators
7. **Agent Monitoring Dashboard:**
[Monitor agents with the Agent Monitoring Dashboard (preview)](https://learn.microsoft.com/en-us/azure/ai-foundry/observability/how-to/how-to-monitor-agents-dashboard) – Setup guide for continuous evaluation in Foundry portal
[Monitor agents with the Agent Monitoring Dashboard (preview)](https://learn.microsoft.com/en-us/azure/foundry/observability/how-to/how-to-monitor-agents-dashboard) – Setup guide for continuous evaluation in Foundry portal
### Sekundærkilder (Community & Research)

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skills/ms-ai-engineering/references/mlops-genaiops/model-evaluation-frameworks.md
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@ -473,15 +473,15 @@ Hvis du kjører massive evalueringer (100K+ samples), vurder PTU for judge model
## Kilder og verifisering
### Microsoft Learn (Verified via MCP)
1. [Evaluate generative AI models and applications by using Microsoft Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/evaluate-generative-ai-app?view=foundry-classic) — **Verified** — Komplett guide til Foundry UI evaluations, metrics, data mapping.
1. [Evaluate generative AI models and applications by using Microsoft Foundry](https://learn.microsoft.com/en-us/azure/foundry/how-to/evaluate-generative-ai-app?view=foundry-classic) — **Verified** — Komplett guide til Foundry UI evaluations, metrics, data mapping.
2. [Evaluation flows and metrics (Azure ML Prompt Flow)](https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-develop-an-evaluation-flow?view=azureml-api-2) — **Verified** — Custom evaluation flows, aggregation nodes.
3. [MLflow 3 Evaluation and Monitoring](https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/eval-monitor/) — **Verified** — LLM judges, scorers, production monitoring.
4. [Large language model end-to-end evaluation](https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/rag/rag-llm-evaluation-phase) — **Verified** — RAG-specific metrics (utilization, completeness, relevance).
5. [Azure AI Evaluation SDK Overview](https://learn.microsoft.com/en-us/python/api/overview/azure/ai-evaluation-readme?view=azure-python) — **Verified** — Python SDK examples, evaluator initialization.
6. [Test and evaluate AI workloads on Azure](https://learn.microsoft.com/en-us/azure/well-architected/ai/test) — **Verified** — Quality metrics, testing vs. evaluation, baselining strategy.
7. [Observability in generative AI](https://learn.microsoft.com/en-us/azure/ai-foundry/concepts/observability) — **Verified** — Three-stage evaluation (base model selection, pre-production, production).
8. [Azure OpenAI Evaluation API](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/evaluations?view=foundry-classic) — **Verified** — REST API, testing criteria, grading process.
9. [GitHub Action for Evaluation](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/evaluation-github-action?view=foundry-classic) — **Verified** — CI/CD integration.
7. [Observability in generative AI](https://learn.microsoft.com/en-us/azure/foundry/concepts/observability) — **Verified** — Three-stage evaluation (base model selection, pre-production, production).
8. [Azure OpenAI Evaluation API](https://learn.microsoft.com/en-us/azure/foundry-classic/openai/how-to/evaluations?view=foundry-classic) — **Verified** — REST API, testing criteria, grading process.
9. [GitHub Action for Evaluation](https://learn.microsoft.com/en-us/azure/foundry/how-to/evaluation-github-action?view=foundry-classic) — **Verified** — CI/CD integration.
10. [Scorers and LLM judges (MLflow 3)](https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/eval-monitor/concepts/scorers) — **Verified** — Judge models, accuracy validation, partner-powered AI disclaimers.
### Confidence per seksjon

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skills/ms-ai-engineering/references/mlops-genaiops/model-versioning-registry-management.md
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@ -541,7 +541,7 @@ az ml model list --registry-name my-registry --query "[?created<'$cutoff_date'].
- Coverage: CI/CD integration, Azure Pipelines, MLOps automation
6. **Explore Microsoft Foundry Models**
- URL: https://learn.microsoft.com/en-us/azure/ai-foundry/concepts/foundry-models-overview?view=foundry-classic
- URL: https://learn.microsoft.com/en-us/azure/foundry-classic/concepts/foundry-models-overview?view=foundry-classic
- Confidence: **Verified** (MCP search results, Feb 2026)
- Coverage: Model catalog, deployment options, Azure AI Foundry integration

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skills/ms-ai-engineering/references/mlops-genaiops/prompt-flow-production-deployment.md
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@ -647,9 +647,9 @@ Er dette første gang kunden deployer LLM-basert app?
## Kilder og verifisering
**Microsoft Learn Dokumentasjon:**
1. [Deploy a flow for real-time inference (Azure AI Foundry)](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/flow-deploy?view=foundry-classic) – Offisiell guide for deployment via portal
1. [Deploy a flow for real-time inference (Azure AI Foundry)](https://learn.microsoft.com/en-us/azure/foundry-classic/how-to/flow-deploy?view=foundry-classic) – Offisiell guide for deployment via portal
2. [GenAIOps with Prompt Flow and GitHub](https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-end-to-end-llmops-with-prompt-flow?view=azureml-api-2) – CI/CD pipeline patterns og lifecycle management
3. [Enable tracing and collect feedback for a flow deployment](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/develop/trace-production-sdk?view=foundry-classic) – Application Insights integration og metrics
3. [Enable tracing and collect feedback for a flow deployment](https://learn.microsoft.com/en-us/azure/foundry-classic/how-to/develop/trace-production-sdk?view=foundry-classic) – Application Insights integration og metrics
4. [Deploy a flow to online endpoint with CLI/SDK](https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-deploy-to-code?view=azureml-api-2) – Advanced deployment configuration (concurrency, FastAPI, etc.)
5. [Integrate Prompt Flow with DevOps](https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-integrate-with-llm-app-devops?view=azureml-api-2) – Local-to-cloud development workflow