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docs(architect): weekly KB update — 52 files refreshed (2026-04)

Browse source 
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>
This commit is contained in:
Kjell Tore Guttormsen 2026-04-10 11:31:11 +02:00
commit 34c6db36fa
40 changed files with 398 additions and 239 deletions
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plugins/ms-ai-architect/skills/ms-ai-advisor/references/copilot-extensibility/copilot-analytics-and-usage-insights.md
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@ -1,6 +1,6 @@
# Copilot Analytics and Usage Monitoring
**Last updated:** 2026-02
**Last updated:** 2026-04
**Status:** GA
**Category:** Copilot Extensibility & Integration
@ -39,6 +39,8 @@ Rapportering av Copilot-bruk skiller seg fra tradisjonell Microsoft 365-rapporte
- Prompts submitted (totalt og gjennomsnitt per bruker)
- Adoption by app (Teams, Outlook, Word, Excel, PowerPoint, OneNote, Loop)
- Last activity date per bruker per app
- **Copilot Chat adoption:** Viser bruk av Copilot Chat (work) og Copilot Chat (web) separat (Verified 2026-04)
- **Agent adoption:** Viser aktive brukere av agenter bygget av din organisasjon (inkl. admin-godkjente og brukeropprettede agenter) (Verified 2026-04)
**Oppdateringsfrekvens:** Data tilgjengelig innen 72 timer etter aktivitet (UTC).
@ -80,6 +82,8 @@ For compliance og security auditing:
**Merk:** Microsoft Purview audit logs inneholder faktiske prompts brukere sender til Copilot. For offentlig sektor er dette særlig sensitivt — implementer access controls for hvem som kan lese audit logs.
**Nytt (Verified 2026-04):** Audit logs for **ikke-Microsoft AI-applikasjoner** bruker nå pay-as-you-go billing (180 dagers oppbevaring, fakturert per antall audit records). Microsoft 365 Copilot og Copilot Studio er fortsatt inkludert i Audit Standard uten ekstra kostnad.
**Søk:**
```plaintext
Purview portal > Solutions > Audit > Workloads: AIApp + Copilot
@ -461,8 +465,8 @@ Get-MgBetaReportMicrosoft365CopilotUserCountSummary `
**Microsoft Learn (Verified – MCP research 2026-04):**
- [Microsoft 365 Copilot reporting options for admins](https://learn.microsoft.com/en-us/copilot/microsoft-365/microsoft-365-copilot-reports-for-admins)
- [Microsoft 365 Copilot usage report](https://learn.microsoft.com/en-us/microsoft-365/admin/activity-reports/microsoft-365-copilot-usage)
- [Microsoft 365 Copilot readiness report](https://learn.microsoft.com/en-us/microsoft-365/admin/activity-reports/microsoft-365-copilot-readiness)
- [Microsoft 365 Copilot usage report](https://learn.microsoft.com/en-us/microsoft-365/admin/activity-reports/microsoft-365-copilot-usage) — Inkluderer nå Agent adoption-seksjon og Copilot Chat (work/web) split (Verified 2026-04)
- [Microsoft 365 Copilot readiness report](https://learn.microsoft.com/en-us/microsoft-365/admin/activity-reports/microsoft-365-copilot-readiness) — Viser 'Suggested candidate for Copilot' (topp 25% ikke-lisensierte brukere basert på M365-bruk) (Verified 2026-04)
- [Connect to the Microsoft Copilot Dashboard](https://learn.microsoft.com/en-us/viva/insights/org-team-insights/copilot-dashboard)
- [Copilot Analytics introduction](https://learn.microsoft.com/en-us/viva/insights/copilot-analytics-introduction)
- [Microsoft Purview audit logs for Copilot](https://learn.microsoft.com/en-us/purview/audit-copilot)

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plugins/ms-ai-architect/skills/ms-ai-advisor/references/copilot-extensibility/copilot-studio-nlp-configuration.md
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@ -1,6 +1,6 @@
# NLP Configuration and Intent Recognition
**Last updated:** 2026-02
**Last updated:** 2026-04
**Status:** GA
**Category:** Copilot Extensibility & Integration
@ -57,7 +57,7 @@ Trigger phrases er eksempelsetninger som definerer når en topic skal aktiveres.
|------|-------------|---------------|
| **Prebuilt entities** | Microsoft-vedlikeholdte typer (Age, Date, Money, Phone, Email, Location, etc.) | Ingen konfigurasjon nødvendig |
| **Closed list entities** | Predefinerte verdier med synonymer | Manuell liste (f.eks. produktkategorier) |
| **Regex entities** | Mønsterbasert ekstraksjon | Regular expressions (f.eks. ordrenummer, referansekoder) |
| **Regex entities** | Mønsterbasert ekstraksjon | Regular expressions. NLU/CLU bruker .NET regex-syntaks; NLU+ bruker JavaScript regex-syntaks (Verified 2026-04) |
| **Learned entities (NLU+/CLU)** | Kontekstbasert ekstraksjon via maskinlæring | Krever annoterte treningsdata |
**Entity annotations** (NLU+):
@ -330,7 +330,7 @@ const results = await client.analyze("KeyPhraseExtraction", documents);
### Språkkrav og GDPR-compliance
**Norsk språkstøtte:**
- **Generative Orchestration**: Støtter norsk (nb-NO) ✅ — automatisk generert innhold oversettes dynamisk; agenten kan bytte språk per samtaletur (dynamic language switching, GA jun 2025)
- **Generative Orchestration**: Støtter norsk (nb-NO) ✅ — automatisk generert innhold oversettes dynamisk; agenten kan bytte språk per samtaletur (dynamic language switching). Merk: Primærspråket kan ikke endres etter opprettelse, men region kan justeres. (Verified 2026-04)
- **Built-in NLU**: Støtter norsk (nb-NO) ✅
- **NLU+**: Støtter norsk (nb-NO) ✅ (avansert NLU-tilpasning tilgjengelig fra jul 2025)
- **Azure CLU**: Støtter norsk (nb-NO) ✅
@ -530,11 +530,11 @@ Følgende Microsoft Learn-dokumentasjon ble brukt (april 2026):
5. **Use entities and slot filling in agents**
- URL: https://learn.microsoft.com/en-us/microsoft-copilot-studio/advanced-entities-slot-filling
- Dekker: Prebuilt entities, custom entities, slot filling, proactive slot filling
- Dekker: Prebuilt entities, custom entities (closed list + regex), slot filling, proactive slot filling, multiple entity recognition (maks 5 per Question-node), entity literals (Verified 2026-04)
6. **Configure and create multilingual agents**
- URL: https://learn.microsoft.com/en-us/microsoft-copilot-studio/multilingual
- Dekker: System.User.Language, auto-detect language, localization best practices, dynamic language switching (generative orchestration), secondary language management
- Dekker: System.User.Language, auto-detect language, localization best practices, dynamic language switching (generative orchestration), secondary language management. Primærspråk kan ikke endres etter opprettelse (Verified 2026-04)
7. **Code samples**
- microsoft_code_sample_search: Entity extraction, trigger phrases, YAML topic definitions
@ -563,6 +563,7 @@ Alle "Verified"-markeringer er basert på:
- **Dokument opprettet**: 2026-02-04
- **MCP-data hentet**: 2026-04-10
- **Siste innholdsoppdatering**: 2026-04
- **Microsoft Learn-versjon**: April 2026
- **Copilot Studio-versjon**: GA (Generally Available)

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plugins/ms-ai-architect/skills/ms-ai-advisor/references/copilot-extensibility/copilot-studio-topics-and-entities.md
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@ -1,6 +1,6 @@
# Topics and Entities in Copilot Studio
**Last updated:** 2026-02
**Last updated:** 2026-04
**Status:** GA
**Category:** Copilot Extensibility & Integration
@ -40,7 +40,7 @@ Topics kan opprettes manuelt, ved AI-assistert beskrivelse (Copilot-generering),
|------|-------------|------|
| **Prebuilt entities** | 30+ innebygde typer: age, boolean, city, color, country, date/time, email, money, number, phone, URL, etc. | Direkte tilgjengelig via entity picker i Question-noder |
| **Closed list entities** | Egendefinert liste med verdier og synonymer (f.eks. "hiking" med synonymer "trekking", "mountaineering") | Best for små, oversiktlige lister med forutsigbare verdier |
| **Regex entities** | Mønsterbasert matching med regulære uttrykk | For strukturerte formater som ordre-ID (INC000001), lisensplater, IP-adresser |
| **Regex entities** | Mønsterbasert matching med regulære uttrykk | For strukturerte formater som ordre-ID (INC000001), lisensplater, IP-adresser. NLU/CLU bruker .NET regex; NLU+ bruker JavaScript regex (Verified 2026-04) |
| **Smart matching** | Fuzzy logic for stavefeil og semantisk utvidelse (f.eks. "softball" → "baseball") | Aktiveres per closed list entity |
| **External entities** | Importerte entities fra CLU (Conversational Language Understanding) med custom JSON resolutions | For avanserte NLU-scenarier med komplekse datatyper |
@ -115,7 +115,9 @@ Agent gjenkjenner automatisk:
Agent hopper over allerede besvarte spørsmål.
**Merk (oppdatert 2026):** Proactive slot filling er aktivert som standard. Deaktiver per node via **Skip question → Ask every time** i Question-noden Properties. Agenten lytter aktivt og husker informasjon gjennom hele samtalen.
**Merk (oppdatert 2026-04):** Proactive slot filling er aktivert som standard. Deaktiver per node via **Skip question → Ask every time** i Question-noden Properties. Agenten lytter aktivt og husker informasjon gjennom hele samtalen.
**Entity literals (Verified 2026-04):** Du kan eksponere eksakt ordlyd fra bruker-input (f.eks. "tomorrow") ved å aktivere **Include metadata** i Question-nodens entity recognition-properties. Variabelen blir da av typen *record* med (råtekst fra bruker) og (strukturert verdi). Nyttig for naturlige bekreftelsesmeldinger («Du bestilte for i morgen (4/2/2026)»).
**Arkitekturvalg:**
@ -126,7 +128,7 @@ Agent hopper over allerede besvarte spørsmål.
#### 5. Multiple Entity Recognition
En Question-node kan akseptere opptil 5 forskjellige entities:
En Question-node kan akseptere opptil 5 forskjellige entities (Verified 2026-04):
```yaml
- kind: Question
@ -235,7 +237,7 @@ Topics kan publiseres til eksterne kanaler (SMS, Facebook, Slack, WhatsApp) via
3. **Watermark** tracker turntaking i samtalen
4. **Token refresh** kreves hver 30. minutt (håndteres i relay-logikk)
**WhatsApp (GA jul 2025):** Copilot Studio støtter nå direkte publisering til WhatsApp-nummer — ingen mellomlagring via Azure Bot Service nødvendig.
**WhatsApp (GA jul 2025):** Copilot Studio støtter direkte publisering til WhatsApp-nummer — ingen mellomlagring via Azure Bot Service nødvendig. (Verified 2026-04)
---

2
plugins/ms-ai-architect/skills/ms-ai-advisor/references/copilot-extensibility/declarative-agents-fundamentals.md
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@ -197,7 +197,7 @@ Declarative agents bruker en konfigurasjonsdrevet tilnærming i stedet for custo
**Verified:** ISV store submission krav (Microsoft 365 validation guidelines):
- Minst 3 prompt starters (conversation_starters) — Must fix
- App manifest versjon 1.13 eller nyere — Must fix
- App manifest versjon 1.13 eller nyere — Must fix (seneste GA-versjon: 1.26, april 2026) (Verified 2026-04)
- Navn (`name`) MÅ være identisk i manifest.json, declarativeAgent.json og plugin.json — Must fix
- Responstid ≤9 sekunder (99 percentil) — Must fix
- Alle serverkall med HTTPS + TLS 1.2+ — Must fix

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plugins/ms-ai-architect/skills/ms-ai-advisor/references/copilot-extensibility/declarative-agents-grounding-strategies.md
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@ -22,7 +22,8 @@ Denne guiden dekker arkitekturmønstre for grounding, beslutningskriterier for v
| Knowledge Source | Beskrivelse | Lisenskrav | Scoping (generativ modus) |
|-----------------|-------------|------------|---------------------------|
| **SharePoint** | Filer, mapper, sites i SharePoint Online | Microsoft 365 Copilot-lisens | 25 URLer (klassisk: 4) |
| **SharePoint** | Filer, mapper, sites i SharePoint Online | Microsoft 365 Copilot-lisens | 25 URLer (klassisk: 4) | (Verified 2026-04)
| **Documents (opplastede filer)** | Filer lastet opp direkte til agenten (lagres i Dataverse) | Microsoft 365 Copilot-lisens eller metered usage | Generativ: Ubegrenset; klassisk: begrenset av Dataverse-kvote |
| **OneDrive** | Brukerens OneDrive-innhold | Microsoft 365 Copilot-lisens | Ja (via manifest) |
| **Copilot Connectors** | Eksterne systemer (ServiceNow, Salesforce, etc.) via Microsoft Graph | Microsoft 365 Copilot-lisens | Ubegrenset (klassisk: 2) |
| **Teams Messages** | Chat-historikk, meeting transcripts, kanal-meldinger | Microsoft 365 Copilot-lisens | Opptil 5 chats |
@ -269,7 +270,9 @@ Hvis tenant har **Microsoft 365 Copilot-lisens**, aktiver **Tenant graph groundi
**Trade-off:** Noe høyere latency for enkelte queries. Kan slås av per agent hvis kvaliteten er lavere enn forventet.
**Innholdsstyring (Official sources):** Kunnskapskilder kan merkes som "official source" — agenten indikerer dette i svar. Merk: Per april 2026 er official sources-funksjonen ikke kompatibel med generativ orchestration (krever klassisk modus).
**Innholdsstyring (Official sources):** Kunnskapskilder kan merkes som "official source" — agenten indikerer dette i svar. Merk: Per april 2026 er official sources-funksjonen ikke kompatibel med generativ orchestration (krever klassisk modus). (Verified 2026-04)
**Allow ungrounded responses (Verified 2026-04):** Ny innstilling i Generative AI-settings. Når den er AV (default), blokkeres svar der agenten ikke brukte noen knowledge source eller tool i det gjeldende svaret. Gir strammere grounding, men kan blokkere follow-up-svar basert på samtalehistorikk.
### Copilot Connectors vs Power Platform Connectors
@ -305,7 +308,7 @@ Kun støttet via **Agents Toolkit** (ikke Agent Builder i Microsoft 365 Copilot
| Web Search (Bing) | USA (Bing service) | ⚠️ **DPA gjelder ikke** | ❌ Ikke for sensitive queries |
| Dataverse (EU tenant) | EU (Norway/West Europe) | ✅ GDPR-compliant | ✅ OK for offentlig sektor |
**Kritisk forskjell:** Web Search-queries sendes til Bing og er **ikke** dekket av Microsoft DPA for enterprise. For sensitive queries, **ikke bruk Web Search**.
**Kritisk forskjell:** Web Search-queries sendes til Bing og er **ikke** dekket av Microsoft DPA for enterprise. For sensitive queries, **ikke bruk Web Search**. (Verified 2026-04: Bruk informasjon fra nettet bruker «Grounding with Bing Search»)
### AI Act Compliance
@ -429,7 +432,7 @@ Tenant graph grounding krever **minst én Microsoft 365 Copilot-lisens** i tenan
## Kilder og verifisering
### Microsoft Learn (Verified — MCP research 2026-02)
### Microsoft Learn (Verified — MCP research 2026-04)
- [Add knowledge sources to your declarative agent](https://learn.microsoft.com/en-us/microsoft-365-copilot/extensibility/knowledge-sources) — Oversikt over alle knowledge sources
- [Add knowledge sources in Agent Builder](https://learn.microsoft.com/en-us/microsoft-365-copilot/extensibility/agent-builder-add-knowledge) — UI-guide for Agent Builder
@ -437,7 +440,7 @@ Tenant graph grounding krever **minst én Microsoft 365 Copilot-lisens** i tenan
- [Declarative agent manifest v1.6](https://learn.microsoft.com/en-us/microsoft-365-copilot/extensibility/declarative-agent-manifest-1.6) — JSON-syntax for knowledge sources
- [Microsoft 365 Copilot connectors overview](https://learn.microsoft.com/en-us/microsoft-365-copilot/extensibility/overview-copilot-connector) — Graph connectors for external data
- [Copilot Studio: Add Copilot connectors as knowledge](https://learn.microsoft.com/en-us/microsoft-copilot-studio/knowledge-copilot-connectors) — Copilot Studio-spesifikk guide
- [Copilot Studio: Knowledge sources summary](https://learn.microsoft.com/en-us/microsoft-copilot-studio/knowledge-copilot-studio) — Inkludert Tenant graph grounding
- [Copilot Studio: Knowledge sources summary](https://learn.microsoft.com/en-us/microsoft-copilot-studio/knowledge-copilot-studio) — Inkludert Tenant graph grounding, Allow ungrounded responses, Web Search (Verified 2026-04)
- [Data, privacy, and security for web search](https://learn.microsoft.com/en-us/microsoft-copilot-studio/data-privacy-security-web-search) — Bing integration, GDPR, DPA
- [Quotas and limits for Copilot Studio](https://learn.microsoft.com/en-us/microsoft-copilot-studio/requirements-quotas) — File size, connector limits

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plugins/ms-ai-architect/skills/ms-ai-advisor/references/copilot-extensibility/m365-copilot-plugins-ecosystem.md
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@ -33,8 +33,8 @@ M365 Copilot plugins distribueres som en `.zip`-fil som inneholder:
```json
{
"$schema": "https://developer.microsoft.com/json-schemas/teams/v1.18/MicrosoftTeams.schema.json",
"manifestVersion": "1.18",
"$schema": "https://developer.microsoft.com/json-schemas/teams/v1.26/MicrosoftTeams.schema.json",
"manifestVersion": "1.26" // Verified 2026-04: v1.26 er seneste GA (april 2026),
"version": "1.0.0",
"id": "00000000-0000-0000-0000-000000000000",
"developer": {
@ -247,7 +247,7 @@ App Manifest (manifest.json)
| Krav | Verdi |
|------|-------|
| **Manifest-versjon** | 1.13 eller nyere |
| **Manifest-versjon** | 1.13 eller nyere (seneste GA: v1.26, april 2026) (Verified 2026-04) |
| **Responstid** | ≤9 sek (99%), ≤5 sek (75%), ≤2 sek (50%) |
| **Tilgjengelighet** | 99.9% uptime |
| **TLS** | 1.2 eller høyere (alle serverkall) |
@ -432,7 +432,7 @@ Hvis plugin brukes til å fatte avgjørelser som påvirker individers rettighete
| **Ecosystem Overview** | [Copilot extensibility in the Microsoft 365 ecosystem](https://learn.microsoft.com/microsoft-365-copilot/extensibility/ecosystem) | ✅ Verified (MCP) |
| **App Package Structure** | [Agents are apps for Microsoft 365](https://learn.microsoft.com/microsoft-365-copilot/extensibility/agents-are-apps) | ✅ Verified (MCP) |
| **Distribution Methods** | [Publish agents for Microsoft 365 Copilot](https://learn.microsoft.com/microsoft-365-copilot/extensibility/publish) | ✅ Verified (MCP) |
| **Manifest Schema** | [Microsoft 365 app manifest schema reference](https://learn.microsoft.com/microsoft-365/extensibility/schema) | ✅ Verified (MCP) |
| **Manifest Schema** | [Microsoft 365 app manifest schema reference](https://learn.microsoft.com/microsoft-365/extensibility/schema) — seneste GA: v1.26 (april 2026) | ✅ Verified (MCP 2026-04) |
| **Plugin Types** | [Adopt, extend and build Copilot experiences](https://learn.microsoft.com/copilot/roadmap/overview) | ✅ Verified (MCP) |
| **Teams Admin Center** | [Manage apps in Teams admin center](https://learn.microsoft.com/microsoftteams/manage-apps) | ✅ Verified (MCP) |
| **Partner Center** | [Microsoft 365 and Copilot program](https://learn.microsoft.com/partner-center/marketplace/why-publish) | ✅ Verified (MCP) |
@ -446,4 +446,4 @@ Hvis plugin brukes til å fatte avgjørelser som påvirker individers rettighete
- ✅ **Verified:** Hentet direkte fra Microsoft Learn via MCP (oppdatert per januar 2026)
- ⚠️ **Baseline:** Basert på modellkunnskap (legal/regulatory tekster, ikke Microsoft-dokumentasjon)
**Siste oppdatering av Microsoft-dokumentasjon:** April 2026 (reflektert i MCP-kall 2026-04-10)
**Siste oppdatering av Microsoft-dokumentasjon:** April 2026 (reflektert i MCP-kall 2026-04-10) — Manifest v1.26 GA

8
plugins/ms-ai-architect/skills/ms-ai-advisor/references/copilot-extensibility/sharepoint-copilot-agents.md
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@ -33,7 +33,7 @@ SharePoint Copilot Agents bruker samme AI-fundamentet som Microsoft 365 Copilot
| Modell | Beskrivelse | Tilgang |
|--------|-------------|---------|
| **Microsoft 365 Copilot license** | Full tilgang til SharePoint Copilot Agents + Microsoft 365 Copilot i alle apper. | Alle agenter er inkludert uten ekstra kostnad. |
| **Pay-as-you-go billing** | Azure-basert betaling per query for brukere uten Copilot-lisens. | Krever Azure-ressurs og billing policy tilknyttet en sikkerhetsgruppe — kun brukere i gruppen får tilgang. |
| **Pay-as-you-go billing** | Azure-basert betaling per query for brukere uten Copilot-lisens. | Krever Azure-ressurs og billing policy (security group). Kun brukere i den angitte sikkerhetsgruppen får tilgang. (Verified 2026-04) |
| **Trial promotion (6 måneder)** | 10 000 queries/måned gratis for unlicensed users. | Automatisk når pay-as-you-go er aktivert. |
**Praktisk eksempel:**
@ -169,8 +169,8 @@ SharePoint Copilot Agents respekterer **eksisterende SharePoint-permissions og s
### SharePoint + Copilot Chat (M365 Copilot)
- Agenter opprettet i SharePoint kan brukes i **Copilot Chat** hvis brukeren har M365 Copilot-lisens.
- Tenant admins og AI-admins kan **blokkere** spesifikke agenter fra Copilot Chat via **Copilot Control System** i M365 admin center (under **Agents**-seksjonen).
- **Limitation (Verified):** Blocking via admin center påvirker kun Copilot Chat — det gjelder IKKE for OneDrive, SharePoint eller Teams.
- Tenant admins og AI-admins kan **blokkere** spesifikke agenter fra Copilot Chat via **Copilot Control System** i M365 admin center (under **Agents**-seksjonen). Dette gir en oversikt over alle agenter som noen gang er brukt i Copilot Chat, med mulighet til å vise detaljer og blokkere/åpne agenter.
- **Limitation (Verified 2026-04):** Blocking via admin center påvirker kun Copilot Chat — det gjelder IKKE for OneDrive, SharePoint eller Teams.
- AI Admin er en ny, dedikert rolle for agent-administrasjon (less privileged enn Global Admin).
### SharePoint + OneDrive
@ -328,7 +328,7 @@ SharePoint Copilot Agents respekterer **eksisterende SharePoint-permissions og s
### Microsoft Learn-kilder (Verified)
1. [Get started with agents in SharePoint](https://learn.microsoft.com/en-us/sharepoint/get-started-sharepoint-agents) — **Verified** (apr 2026)
2. [Manage access to agents in SharePoint](https://learn.microsoft.com/en-us/sharepoint/manage-access-agents-in-sharepoint) — **Verified** (apr 2026)
2. [Manage access to agents in SharePoint](https://learn.microsoft.com/en-us/sharepoint/manage-access-agents-in-sharepoint) — **Verified** (apr 2026) — Oppdatert: per-user lisensstyring, pay-as-you-go med security groups, restricted content discovery, DLP for .agent-filer
3. [Microsoft 365 Copilot agents admin guide](https://learn.microsoft.com/en-us/copilot/microsoft-365/agent-essentials/m365-agents-admin-guide) — **Verified** (apr 2026)
4. [Declarative agents for Microsoft 365 Copilot](https://learn.microsoft.com/en-us/microsoft-365-copilot/extensibility/overview-declarative-agent) — **Verified** (feb 2026)
5. [Publish agents for Microsoft 365 Copilot](https://learn.microsoft.com/en-us/microsoft-365-copilot/extensibility/publish) — **Verified** (feb 2026)

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plugins/ms-ai-architect/skills/ms-ai-advisor/references/platforms/copilot-studio.md
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@ -1,6 +1,6 @@
# Microsoft Copilot Studio - Knowledge Base
**Last updated:** 2026-02 (research via microsoft-learn MCP)
**Last updated:** 2026-04 (research via microsoft-learn MCP)
**Status:** GA (General Availability)
---
@ -53,7 +53,7 @@ Agenter som kjører i bakgrunnen uten brukerinput:
Copilot Studio støtter nå Computer-Using Agents (CUA) — AI som kan interagere med Windows-applikasjoner og nettsider via virtuell mus og tastatur:
- **Beskriv oppgaven med naturlig språk** — agenten utfører oppgaven automatisk
- **Støttede modeller:** OpenAI Computer-Using Agent og Anthropic Claude Sonnet 4.5 (beta, feb 2026)
- **Støttede modeller:** OpenAI Computer-Using Agent og Anthropic Claude Sonnet 4.5 — Verified (MCP 2026-04)
- **Bruksscenarier:** Automatisk datainntasting, fakturabehandling, dataekstraksjon fra apper uten API
- **Fakturering:** 5 Copilot Credits per steg i agentens kjøring
- **Krav:** Generative orchestration aktivert; dedicated Windows-maskin (isolert)
@ -120,8 +120,14 @@ AI-drevet orkestrering som automatisk velger:
- Passende knowledge sources
**Modi:**
- **Classic**: Manuell topic-matching
- **Generative**: AI velger automatisk (anbefalt)
- **Classic**: Manuell topic-matching basert på trigger phrases
- **Generative**: AI velger automatisk (anbefalt) — Verified (MCP 2026-04)
**Tilleggskontroller for generative orchestration:**
- **End all topics**-node: Avbryt gjenstående trinn i orkestreringsplanen
- **AI response generated** trigger: Utløses når agent genererer svar
- **Plan complete** trigger: Utløses når agent har fullført alle trinn
- **Clear variable values** node med «Conversation history for the current session»: Nullstill samtalehistorikk (nyttig for kanaler som Teams med lang historikk) — Verified (MCP 2026-04)
---
@ -171,9 +177,9 @@ CUA lar agenter automatisere oppgaver i Windows-applikasjoner og nettsider uten
- Konfigureres med naturlig språk — ikke kode
- Adapterer til UI-endringer automatisk
### Støttede modeller (per jan 2026):
### Støttede modeller (per mar 2026):
- OpenAI's Computer-Using Agent
- Anthropic's Claude Sonnet 4.5
- Anthropic's Claude Sonnet 4.5 (beta, ruller ut på tvers av støttede regioner) — Verified (MCP 2026-04)
### Kjøringsmiljøer:
| Type | Beskrivelse | Bruksområde |
@ -213,6 +219,7 @@ Code Interpreter lar agenter generere og kjøre Python-kode i et sandkassemiljø
- **Dataanalyse**: Statistiske beregninger, tabelloperasjoner, joins
- **Visualisering**: Grafer, diagrammer, QR-koder
- **Filbehandling**: Excel, Word, PowerPoint, PDF (les og skriv)
- **Dataverse-tabelldata**: Prosessering av Dataverse-tabelldata — Verified (MCP 2026-04)
- **Matematikk**: Komplekse beregninger
- **Syntetiske data**: Generer testdatasett
@ -231,6 +238,7 @@ Code Interpreter lar agenter generere og kjøre Python-kode i et sandkassemiljø
- Bilder rendres ikke i Teams/M365 Copilot-kanalen
- Kan ikke kalle prompts som tools direkte fra topics
- Sesjonslimitasjoner for langkjørende oppgaver
- Krever at agent er konfigurert med brukerautentisering (Direct line uten autentisering støttes ikke) — Verified (MCP 2026-04)
**Lisensiering:** Teller som "text and generative AI tools (premium)" — forbruker Copilot Credits.
@ -546,7 +554,7 @@ Copilot Studio kan bruke **Bring Your Own Model** fra Azure AI Foundry for custo
- **Tabular data knowledge** fra Dataverse, Salesforce, ServiceNow
### Preview
- **CUA (Computer-Using Agents)** — desktop/web GUI automation (sept 2025; GA mai 2026)
- **CUA (Computer-Using Agents)** — desktop/web GUI automation (sept 2025; GA planlagt mai 2026); jan 2026: Cloud PC pooling, enhanced audit logging med session replay, innebygde credentials — Verified (MCP 2026-04)
- **GPT-5** models (US, okt 2025)
- **A2A (Agent2Agent) protocol** — inter-agent communication
- **Copilot Tuning** — fine-tune M365 Copilot på tenant-data (EAP, Build 2025)
@ -575,7 +583,7 @@ Copilot Studio kan bruke **Bring Your Own Model** fra Azure AI Foundry for custo
### Nyheter mars/april 2026
### Nyheter mars/april 2026 — Verified (MCP 2026-04)
| Feature | Status | Detaljer |
|---------|--------|----------|
@ -583,9 +591,11 @@ Copilot Studio kan bruke **Bring Your Own Model** fra Azure AI Foundry for custo
| **Agent evaluations** | GA | Valider agentytelse med tilpassbare testsett |
| **Multi-turn conversation tests** | GA | Test agenter mot realistiske dialogflyter |
| **ChatGPT-5** | GA (globalt) | Tilgjengelig i produksjonsagenter (unntatt GCC) |
| **Claude Sonnet/Opus modeller** | GA | Claude Sonnet 4.5, 4.6 og Opus globalt tilgjengelig for agenter |
| **Claude Sonnet 4.5, Claude Sonnet 4.6 og Claude Opus** | GA (globalt) | Globalt tilgjengelig (unntatt GCC) — optimaliser resonneringsdybde, kvalitet, latens og kostnad per agent |
| **Bing Custom Search** | GA | Legg til scopet websøk som kunnskapskilde |
| **Post-call action topics** | GA | Trigger backend-handlinger automatisk etter voice-samtale |
| **Prompt assistant** | GA | Utkast til prompts raskere med GPT-modell-forslag i Prompt builder |
| **Tilgjengelighetsretningslinjer for Adaptive Cards** | GA | Støtte for skjermlesere, tastaturnavigasjon og Teams-spesifikke scenarier |
## For Cosmo: Beslutningsveiledning
@ -670,4 +680,4 @@ Adapted from Microsoft Learn documentation ([CC BY 4.0](https://creativecommons.
Content has been translated to Norwegian, reorganized, and augmented with decision guidance.
Research date: 2026-02
Research date: 2026-04

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@ -1,6 +1,6 @@
# Microsoft 365 Copilot - Knowledge Base
**Last updated:** 2026-02 (research via microsoft-learn MCP)
**Last updated:** 2026-04 (research via microsoft-learn MCP)
**Status:** GA (General Availability)
---
@ -195,8 +195,8 @@ Agenter kan nå delegere oppgaver til hverandre i hierarkiske mønstre:
| Mønster | Beskrivelse | Brukscase |
|---------|-------------|-----------|
| **Orchestrator/Subagent** | Primær agent delegerer til spesialiserte sub-agenter | Sales Copilot → Lead Scoring + Proposal agent |
| **Magentic (parallel)** | "Spray and pray" - mange agenter kjøres parallelt | Vibe coding, red teaming |
| **Orchestrator/Subagent** | Primær agent delegerer til spesialiserte sub-agenter («Russian doll»-mønster) | Sales Copilot → Lead Scoring + Proposal agent | Verified (MCP 2026-04)
| **Magentic (parallel)** | «Spray and pray» — mange agenter kjøres parallelt | Vibe coding, red teaming, modellering av nye vaksiner eller kjemiske forbindelser | Verified (MCP 2026-04)
| **Sequential pipeline** | Agenter kjøres i sekvens med definerte steg | Compliance-prosesser |
### Tekniske prinsipper
@ -321,12 +321,14 @@ Krever et kvalifiserende M365-abonnement:
### Security Copilot i M365 E5
**Status:** Inkludert fra november 2025 (rulles ut gradvis)
**Status:** Inkludert fra november 2025 (rulles ut gradvis) Verified (MCP 2026-04)
- Rollout startet 18. november 2025 for eksisterende Security Copilot-kunder med E5
- Alle M365 E5-kunder får tilgang i løpet av 2025-2026 (30 dagers forhåndsvarsel)
- **Kapasitetsmodell:** 400 Security Compute Units (SCU) per 1 000 betalte brukerlisenser, maks 10 000 SCU/mnd uten tilleggskostnad — Verified (MCP 2026-04)
- **12 nye sikkerhetesagenter** inkludert på tvers av Defender, Entra, Intune og Purview
- Zero-click automatisk provisjonering — ingen Azure-oppsett eller kapasitetsprovisjonering
- **Developer Experiences inkludert:** Agent Builder, API-er, MCP- og Graph API-integrasjoner for å bygge egendefinerte agenter og promptbooks — Verified (MCP 2026-04)
**Inkluderte kapabiliteter:**
- All chat, promptbook og agentisk funksjonalitet
@ -437,7 +439,7 @@ Inneholder:
### EU Data Boundary
M365 Copilot støtter EU Data Boundary for organisasjoner med sign-up location i EU/EFTA:
- **Dekker:** Austria, Belgia, Danmark, Finland, Frankrike, Tyskland, Irland, Italia, Nederland, Norge, Polen, Spania, **Sverige**, Sveits + flere
- **Dekker (EU-land):** Austria, Belgia, Bulgaria, Kroatia, Kypros, Tsjekkia, Danmark, Estland, Finland, Frankrike, Tyskland, Hellas, Ungarn, Irland, Italia, Latvia, Litauen, Luxembourg, Malta, Nederland, Polen, Portugal, Romania, Slovakia, Slovenia, Spania, **Sverige**; **(EFTA):** Liechtenstein, Island, Norge, Sveits — Verified (MCP 2026-04)
- **Innhold:** Prompts, responser, organisasjonsdata lagres og prosesseres i europeiske datasentre
### In-Country Processing (2026)
@ -688,4 +690,4 @@ Adapted from Microsoft Learn documentation ([CC BY 4.0](https://creativecommons.
Content has been translated to Norwegian, reorganized, and augmented with decision guidance.
Research date: 2026-02
Research date: 2026-04

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@ -1,6 +1,6 @@
# Power Platform AI - Knowledge Base
**Last updated:** 2026-02
**Last updated:** 2026-04
**Status:** GA (General Availability)
---
@ -64,8 +64,8 @@ AI Builder er nå integrert med Azure Document Intelligence v4.0:
- **Table confidence scores** - Konfidensscoring for tabeller og celler
- **GPT-basert dokumentutvinning** - Trekk ut felt uten forhåndstrening (GA mars 2025)
**Document processing agent (preview fra mai 2025):**
Bruk en dedikert agent til å effektivisere dokumentprosessering i flows, uten manuell modellkonfigurasjon.
**Document processing agent (preview fra mai 2025, GA plan sep 2025):**
Bruk en dedikert agent til å effektivisere dokumentprosessering i flows, uten manuell modellkonfigurasjon. (Verified 2026-04)
```
Pattern: GPT-basert dokumentutvinning
@ -246,8 +246,8 @@ AI-funksjoner for bygging og drift av forretningsnettsteder.
| Feature | Status | Beskrivelse |
|---------|--------|-------------|
| **Svar fra websidedata** | Preview juni 2025 | Brukere kan stille spørsmål og få svar fra nettstedinnholdet |
| **Filtrer lister med naturlig språk** | Preview mai 2025 | Bruker naturlig språk for å filtrere datatabeller |
| **Svar fra websidedata** | Preview juni 2025 | Brukere kan stille spørsmål og få svar fra nettstedinnholdet (Verified 2026-04) |
| **Filtrer lister med naturlig språk** | Preview mai 2025 | Bruker naturlig språk for å filtrere datatabeller (Verified 2026-04) |
| **Sikkerhetsagent** | Preview jan 2026 | Kontekst-bevisst agent for sideikkerhet |
### Power Pages MCP Server
@ -259,6 +259,7 @@ AI-funksjoner for bygging og drift av forretningsnettsteder.
- Aktiver/deaktiver AI-funksjoner per miljø (maker og sluttbruker separat)
- Granulær kontroll per funksjon
- Tenant- og nettstedsnivå-kontroll via Copilot Hub
- **Build modern single-page applications (GA jan 2026):** Støtte for moderne SPA-applikasjoner i Power Pages (Verified 2026-04)
---
@ -344,7 +345,7 @@ Copilot Credits er den unified valutaen for all AI-funksjonalitet i Power Platfo
| Generative answer | 2 credits |
| Agent action | 5 credits |
| Tenant graph grounding | 10 credits |
| Agent flow actions (per 100) | 13 credits |
| Agent flow actions (per 100) | 13 credits | (Verified 2026-04: Ny enforcement: nye agent flow-kjøringer blokkeres ved fullt forbruk; pågående kjøringer fullfører)
| AI tools (basic, per 10 resp) | 1 credit |
| AI tools (standard, per 10 resp) | 15 credits |
| AI tools (premium, per 10 resp) | 100 credits |
@ -417,7 +418,7 @@ Power Platform Admin Center som unified governance hub for agenter:
3. **Managed operations** — Overvåking, alerting, lifecycle management
4. **Managed availability** — Enterprise-grade pålitelighet
**Governance-funksjoner (Wave 2, okt 2025–mars 2026):**
**Governance-funksjoner (Wave 2, okt 2025–mars 2026) (Verified 2026-04):**
- Environment groups og policyer
- Advanced Connector Policies (ACP)
- Tenant-wide inventory med agent-oversikt
@ -425,6 +426,8 @@ Power Platform Admin Center som unified governance hub for agenter:
- Enterprise scale administration med bulk-governance-verktøy
- Granulær lisensinnsikt og kapasitetsovervåking
- Proaktive security-kontroller for AI-agenter
- **Fire søyler:** Managed security, Managed governance, Managed operations, Managed availability
- Power Platform Admin Center som unified governance hub for intelligente agenter og agent-drevne apper
### AI Builder Policies
@ -600,10 +603,10 @@ Adapted from Microsoft Learn documentation ([CC BY 4.0](https://creativecommons.
- [Enhance AI-powered experiences with Dataverse search](https://learn.microsoft.com/power-platform/release-plan/2025wave1/data-platform/enhance-ai-powered-experiences-dataverse-search)
- [Power Platform governance and administration 2025 Wave 1](https://learn.microsoft.com/power-platform/release-plan/2025wave1/power-platform-governance-administration/)
- [Power Platform governance and administration 2025 Wave 2](https://learn.microsoft.com/power-platform/release-plan/2025wave2/power-platform-governance-administration/)
- [Billing rates and management — Copilot Credits](https://learn.microsoft.com/microsoft-copilot-studio/requirements-messages-management) — Oppdatert april 2026: AI tools faktureres per 10 responses (basic=1, standard=15, premium=100 credits); agent flow enforcement gjelder per-miljø med 125%-terskel
- [Billing rates and management — Copilot Credits](https://learn.microsoft.com/microsoft-copilot-studio/requirements-messages-management) — Oppdatert april 2026: AI tools faktureres per 10 responses (basic=1, standard=15, premium=100 credits); agent flow enforcement (blocking ved fullt forbruk, ikke 125%-terskel — 125% gjelder kun generell agent-enforcement); M365 Copilot-lisensierte brukere faktureres ikke (Verified 2026-04)
- [AI Builder licensing](https://learn.microsoft.com/en-us/ai-builder/administer-licensing)
- Power Platform release plans 2025 Wave 1 and Wave 2
Content has been translated to Norwegian, reorganized, and augmented with decision guidance.
Research date: 2026-02
Research date: 2026-04

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@ -45,7 +45,7 @@ Administratorer har 11 lifecycle management actions tilgjengelig i Admin Center:
| **Delete** | Permanent sletting (inkludert SharePoint Embedded containers) | Irreversibel cleanup (24t propagation) |
| **Approve Updates** | Godkjenn nye versjoner før deployment | Change management |
| **Manage Ownerless Agents** | Handling på agenter uten eier | Compliance og sikkerhet |
| **Reassign** | Tildel ny eier til ownerless/active agents | Kontinuitet |
| **Reassign** | Tildel ny eier til ownerless/active agents. Kun støttet for Agent Builder-agenter. Ny eier får full edit/delete-tilgang og tilgang til opplastede filer; forrige eier mister ALL tilgang inkl. lesetilgang. *(Verified MCP 2026-04)* | Kontinuitet |
| **Export Inventory** | Last ned full agent-liste (Excel) | Audit og rapportering |
**Verified (Microsoft Learn, 2026-02)**
@ -198,7 +198,7 @@ For agenter bygget i Agent Builder med embedded files (knowledge sources):
| Feil | Konsekvens | Løsning |
|------|------------|---------|
| Sletter SharePoint Embedded containers manuelt | Agent-functionality breaks | Aldri slett containers i SharePoint admin center |
| Blokkerer Microsoft-pinned agents (Researcher/Analyst) | Blokkerer for HELE tenant (kan ikke scope) | Bruk extensibility settings istedenfor Block |
| Blokkerer Researcher/Analyst feil | Edit users-panelet er deaktivert for disse agentene; de kan kun blokkeres for hele tenant. Scope til enkeltbrukere er ikke mulig. *(Verified MCP 2026-04)* | Bruk Block-action i Admin Center for hele tenant; Work-access styres separat via admin-innstillinger |
| Glemmer å approve agent updates | Brukere får ikke nye features/bugfixes | Sett opp notification for pending approvals |
| Ingen policy template ved aktivering | Agents opererer uten governance controls | Alltid bruk minimum Default Template |
@ -354,7 +354,7 @@ New-MgIdentityGovernanceLifecycleWorkflow -BodyParameter $params
## Kilder og verifisering
### Microsoft Learn (Verified, 2026-02)
- [Agent Registry i Microsoft 365 Admin Center](https://learn.microsoft.com/en-us/microsoft-365/admin/manage/agent-registry) – **Confidence: Verified (2026-04)** — Oppdatert: Risks column (M365 E7), ownerless agent management, Researcher with Computer Use admin configuration, sensitivity labels for embedded files, GraphAPI for Agent Registry (preview)
- [Agent Registry i Microsoft 365 Admin Center](https://learn.microsoft.com/en-us/microsoft-365/admin/manage/agent-registry) – **Confidence: Verified (2026-04)** — Oppdatert: Risks column (M365 E7), ownerless agent management, Researcher with Computer Use admin configuration, sensitivity labels for embedded files, GraphAPI for Agent Registry (preview), Reassign kun for Agent Builder-agenter, Application/Delegated permissions-tab i agent details
- [Microsoft 365 Copilot Agents Deployment Blueprint](https://learn.microsoft.com/en-us/copilot/microsoft-365/agent-essentials/m365-agents-blueprint) – **Confidence: Verified**
- [Copilot Control System Management Controls](https://learn.microsoft.com/en-us/copilot/microsoft-365/copilot-control-system/management-controls) – **Confidence: Verified**
- [Microsoft Entra Agent ID and Agent Identity Platform](https://learn.microsoft.com/en-us/microsoft-agent-365/admin/capabilities-entra) – **Confidence: Verified**
@ -367,5 +367,5 @@ New-MgIdentityGovernanceLifecycleWorkflow -BodyParameter $params
- **Kostnadsoptimalisering** – Baseline (generelle prinsipper, ikke produkt-spesifikke priser fra Microsoft Learn)
- **Modenhetsnivå-anbefalinger** – Baseline (syntetisert fra Microsoft Maturity Framework-prinsipper)
**Total MCP calls:** 3 (microsoft_docs_search x3, microsoft_docs_fetch x2, microsoft_code_sample_search x1)
**Total MCP calls:** 4 (microsoft_docs_search x3, microsoft_docs_fetch x3, microsoft_code_sample_search x1)
**Unique URLs:** 7 Microsoft Learn-artikler

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@ -172,11 +172,20 @@ A2A skiller mellom **meldinger** (messages) for rask, synkron kommunikasjon, og
```python
import asyncio
import httpx
from a2a.client import A2ACardResolver
from agent_framework.a2a import A2AAgent
async def main():
a2a_host = "https://agents.nav.no/saksbehandler/a2a"
# Discover remote agent capabilities via AgentCard
async with httpx.AsyncClient(timeout=60.0) as http_client:
resolver = A2ACardResolver(httpx_client=http_client, base_url=a2a_host)
agent_card = await resolver.get_agent_card() # Verified MCP 2026-04
# Koble til ekstern A2A-agent
async with A2AAgent(name="saksbehandler", url="https://agents.nav.no/saksbehandler/a2a") as agent:
async with A2AAgent(name=agent_card.name, agent_card=agent_card, url=a2a_host) as agent:
# Synkron streaming
async with agent.run("Hva er min dagpengesats?", stream=True) as stream:
@ -586,6 +595,25 @@ Hver etat eier og drifter sin egen agent. Felles inngangsagent orkestrerer via A
## Installasjon og SDK-er
```python
# Autentisert A2A-kall (AuthInterceptor-mønster) — Verified MCP 2026-04
from a2a.client.auth.interceptor import AuthInterceptor
class BearerAuth(AuthInterceptor):
def __init__(self, token: str):
self.token = token
async def intercept(self, request):
request.headers["Authorization"] = f"Bearer {self.token}"
return request
async with A2AAgent(
name="secure-agent",
url="https://secure-a2a-agent.example.com",
auth_interceptor=BearerAuth("your-token"),
) as agent:
response = await agent.run("Hello!")
```
```bash
# Python — Agent Framework
pip install agent-framework-a2a --pre
@ -603,13 +631,15 @@ pip install microsoft-teams-a2a
**.NET (Semantic Kernel):**
```csharp
// Agent card tilgjengelig på: GET /a2a/my-agent/v1/card
// Message endpoint: POST /a2a/my-agent/v1/message:stream
app.MapA2A(agent, "/a2a/my-agent", agentCard: new()
{
Name = "Min Agent",
Description = "Hjelpsom assistent for norsk offentlig sektor",
Version = "1.0",
Capabilities = new() { Streaming = true }
});
}); // Verified MCP 2026-04
```
---
@ -638,9 +668,9 @@ app.MapA2A(agent, "/a2a/my-agent", agentCard: new()
- https://learn.microsoft.com/azure/api-management/agent-to-agent-api
- Confidence: **Verified** (APIM preview-støtte, februar 2026)
6. **Agent Framework — A2A Integration (Python)**
6. **Agent Framework — A2A Integration (Python og C#)**
- https://learn.microsoft.com/agent-framework/integrations/a2a
- Confidence: **Verified** (offisiell SDK-dokumentasjon, februar 2026)
- Confidence: **Verified (MCP 2026-04)** — A2ACardResolver-pattern (Python), A2AAgent med agent_card-parameter, AuthInterceptor for sikret kall, MapA2A /v1/card og /v1/message:stream endepunkt-paths (.NET), NuGet-pakker Microsoft.Agents.AI.Hosting.A2A og .AspNetCore
7. **Semantic Kernel Agent Orchestration**
- https://learn.microsoft.com/semantic-kernel/frameworks/agent/agent-orchestration/

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@ -107,6 +107,8 @@ if "pending_safety_checks" in response:
Copilot Studio tilbyr CUA som et lavkode **Computer Use Tool** — ingen koding nødvendig.
**Krav (preview):** Tilgjengelig kun for miljøer der regionen er satt til **United States**. Generativ orkestrering (generative orchestrator) må aktiveres på agenten. *(Verified MCP 2026-04)*
### Oppsett
1. Gå til **Tools** i agenten → **Add tool** → **New tool** → **Computer use**
@ -125,9 +127,9 @@ Copilot Studio tilbyr CUA som et lavkode **Computer Use Tool** — ingen koding
|---------------|-------------|
| **Maker-provided credentials** | Agenten bruker makerens innloggingsinfo (for autonome agenter) |
| **End user credentials** | Brukeren logger inn selv (for konversasjonelle agenter) |
| **Intern Power Platform-lagring** | Kryptert intern lagring — ingen forhåndskonfigurasjon nødvendig |
| **Azure Key Vault** | Passord lagres i Key Vault — anbefalt for produksjonsmiljøer |
| **Access control** | Begrens hvilke nettsider/applikasjoner CUA kan operere på |
| **Intern Power Platform-lagring** | Kryptert intern lagring — ingen forhåndskonfigurasjon nødvendig. Oppgi URL/app-navn + brukernavn + passord; wildcard (*) støttes for subdomener (f.eks. *.contoso.com). *(Verified MCP 2026-04)* |
| **Azure Key Vault** | Passord lagres i Key Vault — anbefalt for produksjonsmiljøer. Krever PowerPlatform resource provider registrert i Azure-abonnementet. *(Verified MCP 2026-04)* |
| **Access control** | Begrens hvilke nettsider/applikasjoner CUA kan operere på. Wildcards støttes (*.contoso.com). Desktop apps angis ved produkt-/prosessnavn (f.eks. "Microsoft Edge" eller "msedge"). *(Verified MCP 2026-04)* |
### Lisensiering (Copilot Studio, preview)
@ -485,7 +487,7 @@ Kostnader basert på:
2. **Automate web and desktop apps with computer use — Copilot Studio**
- https://learn.microsoft.com/microsoft-copilot-studio/computer-use
- Confidence: **Verified** (offisiell Copilot Studio preview-dokumentasjon, oppdatert 2026-04: støttede modeller, credentials, access control-semantikk)
- Confidence: **Verified** (offisiell Copilot Studio preview-dokumentasjon, oppdatert 2026-04: støttede modeller, credentials intern/Key Vault-detaljer, access control wildcard/desktop, US-only region-krav, generativ orkestrering påkrevd)
3. **Configure where computer use runs**
- https://learn.microsoft.com/microsoft-copilot-studio/configure-where-computer-use-runs

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@ -209,32 +209,55 @@ var result = await mainAgent.RunAsync("Hvordan er været i Oslo?");
AG-UI backend tool rendering stoetter HITL via to mekanismer:
**C# - ApprovalRequiredAIFunction:**
**C# - AIFunctionFactory med serializerOptions (Verified MCP 2026-04):**
```csharp
// Tool som krever human approval
var approvalTool = ApprovalRequiredAIFunction.Create(DeleteRecord);
// Workflow emitter RequestInfoEvent med ToolApprovalRequestContent
await foreach (var evt in workflow.WatchStreamAsync()) {
if (evt is RequestInfoEvent req && req.Data is ToolApprovalRequestContent tc) {
bool approved = await AskUserApproval(tc.ToolName);
await handle.SendResponseAsync(req.Request.CreateResponse(approved));
}
// Definer tool med Description-attributter
[Description("Search for restaurants in a location.")]
static RestaurantSearchResponse SearchRestaurants(
[Description("The restaurant search request")] RestaurantSearchRequest request)
{
// implementasjon
}
// Registrer tool - NB: serializerOptions PÅKREVD for komplekse typer
var jsonOptions = app.Services.GetRequiredService<IOptions<JsonOptions>>().Value;
AITool[] tools = [
AIFunctionFactory.Create(SearchRestaurants, serializerOptions: jsonOptions.SerializerOptions)
];
// FunctionCallContent og FunctionResultContent streames til klient
// FunctionCallContent: .Name, .Arguments (key-value pairs)
// FunctionResultContent: .CallId, .Result eller .Exception
```
**Python - @tool med approval_mode:**
**Python - @tool decorator (Verified MCP 2026-04):**
```python
@tool(approval_mode="always_require")
def delete_record(record_id: str) -> str:
# Sletter en post - krever alltid menneskelig godkjenning
return db.delete(record_id)
from typing import Annotated
from pydantic import Field
from agent_framework import tool
# Workflow pauser og emitter function_approval_request event
# Klient-loop maa haandtere og respondere
@tool
def get_weather(
location: Annotated[str, Field(description="The city")],
) -> str:
"""Get the current weather for a location."""
return f"The weather in {location} is 22 degrees C."
# Klasse-baserte tools for gruppering
class WeatherTools:
@tool
def get_current_weather(self, location: Annotated[str, Field(description="City")]) -> str:
"""Get current weather."""
return f"Current weather in {location}: Sunny"
```
**Backend tool events streames til klient i sanntid:** TOOL_CALL_START, TOOL_CALL_ARGS, TOOL_CALL_END, TOOL_CALL_RESULT.
**Backend tool events streames til klient i sanntid (Verified MCP 2026-04):**
```json
{"type": "TOOL_CALL_START", "toolCallId": "call_abc123", "toolCallName": "get_weather"}
{"type": "TOOL_CALL_ARGS", "toolCallId": "call_abc123", "delta": "{"location": "Oslo"}"}
{"type": "TOOL_CALL_END", "toolCallId": "call_abc123"}
{"type": "TOOL_CALL_RESULT","toolCallId": "call_abc123", "content": "The weather in Oslo is 22C."}
```
---
@ -428,7 +451,7 @@ def update_citizen_record(ssn: str, field: str, value: str) -> str:
1. [Azure OpenAI Function Calling](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/function-calling) — **Verified 2026-02**
2. [Semantic Kernel Agent Functions](https://learn.microsoft.com/en-us/semantic-kernel/frameworks/agent/agent-functions) — **Verified 2026-02**
3. [Agent Framework - Agent as Function Tool](https://learn.microsoft.com/en-us/agent-framework/tutorials/agents/agent-as-function-tool) — **Verified 2026-02**
4. [AG-UI Backend Tool Rendering](https://learn.microsoft.com/en-us/agent-framework/integrations/ag-ui/backend-tool-rendering) — **Verified 2026-04** (backend tool streaming, ApprovalRequiredAIFunction C#, @tool(approval_mode) Python, TOOL_CALL_* events)
4. [AG-UI Backend Tool Rendering](https://learn.microsoft.com/en-us/agent-framework/integrations/ag-ui/backend-tool-rendering) — **Verified (MCP 2026-04)** — AIFunctionFactory.Create() med serializerOptions for komplekse typer (C#), @tool decorator med Annotated/Field (Python), TOOL_CALL_START/ARGS/END/RESULT events, FunctionCallContent/.Arguments og FunctionResultContent/.Result (C#), klasse-baserte tools-moenster (Python)
5. [Azure OpenAI Assistants Function Calling](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/assistant-functions) — **Verified 2026-02**
6. [Structured Outputs](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/structured-outputs) — **Verified 2026-02**

2
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@ -549,7 +549,7 @@ TOTAL: ~46 700 NOK/måned (høyere cost, men forutsigbar)
3. [Baseline Foundry Chat Architecture (Foundry Agent Service + Microsoft Agent Framework)](https://learn.microsoft.com/en-us/azure/architecture/ai-ml/architecture/baseline-microsoft-foundry-chat) — Verified (MCP 2026-04)
4. [Azure API Management - AI Gateway Capabilities](https://learn.microsoft.com/en-us/azure/api-management/genai-gateway-capabilities)
5. [Reliability in Azure AI Search](https://learn.microsoft.com/en-us/azure/reliability/reliability-ai-search)
6. [Multi-Backend Gateway Guide](https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/azure-openai-gateway-multi-backend) — Verified MCP 2026-04: Dokumentet bekrefter fire gateway-topologier (single instance/multiple deployments, multi-instance same region/subscription, multi-instance multi-region). Tagger nå eksplisitt "Foundry Tools" og "Azure OpenAI in Foundry Models".
6. [Multi-Backend Gateway Guide](https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/azure-openai-gateway-multi-backend) — Verified MCP 2026-04: Dokumentet bekrefter fire gateway-topologier: (1) multiple model deployments i single instance, (2) multiple instances same region/single subscription, (3) multiple instances same region/multiple subscriptions (eksplisitt som egen topologi), (4) multiple instances multi-region. Tagger eksplisitt "Foundry Tools" og "Azure OpenAI in Foundry Models". Anbefaler sterkt credential termination og reestablishment ved gateway fremfor pass-through client credentials. Gateway muliggjør client-based usage tracking for chargeback-modeller. Verified (MCP 2026-04)
7. [Load Balancing Options - Azure Architecture](https://learn.microsoft.com/en-us/azure/architecture/guide/technology-choices/load-balancing-overview)
**GitHub Samples (Microsoft-verified):**

4
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@ -231,7 +231,7 @@ Purview Data Owner Policies muliggjør sentralisert tilgangsstyring:
### Governance Domains og OKR-er
Governance Domains er nå den sentrale organiseringsenhet for glossary terms i Unified Catalog. Workflow: opprett term (Draft) → rediger → publiser. Glossary terms kan flyttes mellom domains (begge domains krever Data Steward-rolle). Termer kan linkes til data products og critical data elements på tvers av domains. *(Verified MCP 2026-04)*
Governance Domains er nå den sentrale organiseringsenhet for glossary terms i Unified Catalog. Workflow: opprett term (Draft) → rediger → publiser. Governance domain MÅ publiseres FØR terms publiseres. Termer kan linkes til data products og critical data elements på tvers av domains. Bulk edit opptil 50 terms (kun Draft-state). Flytt terms mellom domains krever Data Steward-rolle i BEGGE domains; parent-term drar med seg child-terms. Expire-funksjon gjør termen usynlig for alle unntatt Data Stewards og Domain Owners. For å slette: unpublish → fjern alle lenker → delete. *(Verified MCP 2026-04)*
```
Governance Domain: "AI og Maskinlæring"
@ -346,7 +346,7 @@ Microsoft Purview gir nå governance-dekning for Fabric Copilots og agenter —
- [How to get lineage from Microsoft Fabric items into Microsoft Purview](https://learn.microsoft.com/en-us/purview/data-map-lineage-fabric) -- Lineage fra Fabric
- [Data lineage in classic Data Catalog](https://learn.microsoft.com/en-us/purview/data-gov-classic-lineage) -- Lineage-konsepter
- [Learn about sensitivity labels in Data Map](https://learn.microsoft.com/en-us/purview/data-map-sensitivity-labels) -- Sensitivitetsmerking
- [Create and manage glossary terms](https://learn.microsoft.com/en-us/purview/unified-catalog-glossary-terms-create-manage) -- Business glossary *(Verified MCP 2026-04)* — Ny funksjonalitet: bulk edit opptil 50 terms, flytt terms mellom governance domains, custom attributes med filter, Data Steward-rolle påkrevd for opprettelse. Terms opprettes i Draft state, må publiseres for å bli synlige. Governance domain MÅ publiseres FØR terms publiseres.
- [Create and manage glossary terms](https://learn.microsoft.com/en-us/purview/unified-catalog-glossary-terms-create-manage) -- Business glossary *(Verified MCP 2026-04)* — Bulk edit opptil 50 terms (Draft-state), flytt terms mellom governance domains (Data Steward i begge domains kreves), custom attribute-filter i Enterprise glossary, Expire-workflow, Delete-workflow (unpublish + fjern lenker → delete). Governance domain MÅ publiseres FØR terms publiseres. Parent-term drar med seg child-terms ved flytting. Related critical data elements kan linkes på tvers av domains.
- [Glossary terms in Unified Catalog](https://learn.microsoft.com/en-us/purview/unified-catalog-glossary-terms) -- Aktive glossary-termer
- [Learn about Microsoft Purview Unified Catalog](https://learn.microsoft.com/en-us/purview/unified-catalog) -- Oversikt over Unified Catalog
- [Set up data quality for Fabric Lakehouse data](https://learn.microsoft.com/en-us/purview/unified-catalog-data-quality-fabric-lakehouse) -- Datakvalitet for Fabric

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@ -1,6 +1,6 @@
# A/B Testing and Experimentation for AI Models
**Last updated:** 2026-02
**Last updated:** 2026-04
**Verified:** MCP 2026-04
**Status:** GA
**Category:** MLOps & GenAIOps
@ -455,14 +455,15 @@ az ml online-endpoint update --name my-endpoint --traffic control=90 challenger=
# Use RelevanceToQuery, Correctness, custom business scorers
```
**MLflow 3 A/B evaluation pattern**:
**MLflow 3 A/B evaluation pattern** — Verified (MCP 2026-04):
- Use `mlflow.genai.evaluate()` on traces from each variant
- Compare scorers: `Correctness`, `RelevanceToQuery`, `ToolCallEfficiency`
- Compare scorers: `Correctness`, `RelevanceToQuery`, `RetrievalGroundedness`, `ToolCallEfficiency`, `Fluency` — expanded scorer set in MLflow 3
- Multi-turn scorers available: `ConversationCompleteness`, `UserFrustration` for conversational AI A/B testing
- Statistical significance: MLflow tracks Cohen's Kappa against human baseline
- Aliases in Prompt Registry: `@control` and `@challenger` for prompt A/B testing
**Azure ML safe rollout progression**:
1. **Shadow testing**: Mirror X% of traffic to new model (no user impact)
**Azure ML safe rollout progression** — Verified (MCP 2026-04):
1. **Shadow testing**: Mirror X% of traffic to new model (no user impact) — natively supported via `mirror_traffic` property on managed online endpoints
2. **Canary**: Route 10% live traffic, monitor bake time (hours/days)
3. **Progressive**: 10% → 50% → 100% with health gate at each step
4. **Rollback trigger**: Automatic halt on health signal degradation

90
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@ -1,6 +1,6 @@
# Azure ML Pipelines - Orchestration and Automation
**Last updated:** 2026-02
**Last updated:** 2026-04
**Verified:** MCP 2026-04
**Status:** GA
**Category:** MLOps & GenAIOps
@ -22,59 +22,81 @@ Fra et kostnads- og effektivitetsperspektiv gir pipelines betydelige fordeler: d
### Pipeline Components (v2)
### Azure ML Pipelines — Python SDK v2 (Tutorial 2026)
### Azure ML Pipelines — Python SDK v2 (Tutorial, Verified MCP 2026-04)
**Key benefits**: Standardized MLOps, scalable team collaboration, training efficiency, cost reduction.
**Key benefits**: Standardized MLOps practice, scalable team collaboration, training efficiency, cost reduction.
**Pipeline creation pattern** (SDK v2):
**Pipeline creation pattern** (SDK v2 — from official tutorial):
```python
from azure.ai.ml import MLClient, dsl, Input, Output, command
from azure.identity import DefaultAzureCredential
from azure.identity import DefaultAzureCredential, InteractiveBrowserCredential
ml_client = MLClient(DefaultAzureCredential(), subscription_id, resource_group, workspace)
try:
credential = DefaultAzureCredential()
credential.get_token("https://management.azure.com/.default")
except Exception:
credential = InteractiveBrowserCredential()
# 1. Create reusable components
ml_client = MLClient(credential, subscription_id, resource_group, workspace)
# Note: MLClient initialization is lazy — no connection until first call
# 1. Create reusable components (programmatic definition)
data_prep_component = command(
name="data_prep",
name="data_prep_credit_defaults",
inputs={"data": Input(type="uri_folder"), "test_train_ratio": Input(type="number")},
outputs={"train_data": Output(type="uri_folder"), "test_data": Output(type="uri_folder")},
outputs={"train_data": Output(type="uri_folder", mode="rw_mount"),
"test_data": Output(type="uri_folder", mode="rw_mount")},
code="./components/data_prep",
command="python data_prep.py --data ${{inputs.data}} ...",
environment=f"{env.name}:{env.version}",
command="python data_prep.py --data ${{inputs.data}} --test_train_ratio ${{inputs.test_train_ratio}} ...",
environment=f"{pipeline_job_env.name}:{pipeline_job_env.version}",
)
# Register for reuse
ml_client.create_or_update(data_prep_component.component)
data_prep_component = ml_client.create_or_update(data_prep_component.component)
# 2. Define pipeline with @dsl.pipeline decorator
@dsl.pipeline(compute="serverless", description="E2E training pipeline")
def training_pipeline(data_input, test_train_ratio, learning_rate, model_name):
@dsl.pipeline(
compute="serverless", # "serverless" runs on serverless compute
description="E2E data_prep-train pipeline",
)
def credit_defaults_pipeline(data_input, test_train_ratio, learning_rate, registered_model_name):
prep_job = data_prep_component(data=data_input, test_train_ratio=test_train_ratio)
train_job = train_component(
train_data=prep_job.outputs.train_data,
test_data=prep_job.outputs.test_data,
learning_rate=learning_rate,
registered_model_name=model_name,
registered_model_name=registered_model_name,
)
return {
"pipeline_job_train_data": prep_job.outputs.train_data,
"pipeline_job_test_data": prep_job.outputs.test_data,
}
# 3. Submit pipeline
pipeline_job = ml_client.jobs.create_or_update(
training_pipeline(data_input=..., ...),
experiment_name="e2e_pipeline"
credit_defaults_pipeline(
data_input=Input(type="uri_file", path=credit_data.path),
test_train_ratio=0.25,
learning_rate=0.05,
registered_model_name="credit_defaults_model",
),
experiment_name="e2e_registered_components"
)
ml_client.jobs.stream(pipeline_job.name)
```
**Component lifecycle**:
1. Write YAML spec or create programmatically (`CommandComponent`)
2. Register with name+version in workspace or registry
3. Load and compose into pipeline
4. Submit via `ml_client.jobs.create_or_update()`
1. Write YAML spec (`train.yml`) or create programmatically (`CommandComponent` / `command()`)
2. Register with name+version: `ml_client.create_or_update(component)`
3. Load and compose into pipeline using `@dsl.pipeline` decorator
4. Submit via `ml_client.jobs.create_or_update()` with experiment name
**Compute options**: `serverless` (recommended), named compute cluster, or per-step compute override.
**Environment**: Curated environments (`azureml://registries/azureml/environments/sklearn-1.5/labels/latest`) or custom conda/Docker.
**Compute options**: `serverless` (recommended — zero config), named compute cluster, or per-step compute override (e.g., `train_step.compute = "cpu-cluster"`).
**Environment**: Curated environments (`azureml://registries/azureml/environments/sklearn-1.0/labels/latest`) or custom conda/Docker (base image: `mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu22.04:latest`).
**Output types**: `uri_folder` (data), `mlflow_model` (model), `uri_file` (file).
**MLflow integration**: Use `mlflow.start_run()` in scripts for automatic experiment tracking (metrics, parameters, models).
**MLflow integration**: Use `mlflow.start_run()` + `mlflow.sklearn.autolog()` in training scripts for automatic experiment tracking. Models registered via `mlflow.sklearn.log_model()` with `registered_model_name`.
**VNet note**: If workspace uses a managed virtual network, add outbound rules to allow access to public Python package repositories.
| Komponent-type | Beskrivelse | Bruksområde |
|----------------|-------------|-------------|
@ -564,41 +586,41 @@ Er det >3 steg i workflow?
1. **What are Azure Machine Learning pipelines?**
https://learn.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines?view=azureml-api-2
*Confidence: Verified (Feb 2026)*
*Confidence: Verified (April 2026)*
2. **Schedule machine learning pipeline jobs**
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-schedule-pipeline-job?view=azureml-api-2
*Confidence: Verified (Feb 2026)*
*Confidence: Verified (April 2026)*
3. **Create and run machine learning pipelines using components with the Azure Machine Learning SDK v2**
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-component-pipeline-python?view=azureml-api-2
*Confidence: Verified (Feb 2026)*
*Confidence: Verified (April 2026)*
4. **Tutorial: Create production machine learning pipelines**
https://learn.microsoft.com/en-us/azure/machine-learning/tutorial-pipeline-python-sdk?view=azureml-api-2
*Confidence: Verified (Feb 2026)*
*Confidence: Verified (April 2026)*
5. **Use parallel jobs in pipelines**
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-parallel-job-in-pipeline?view=azureml-api-2
*Confidence: Verified (Feb 2026)*
*Confidence: Verified (April 2026)*
6. **Manage inputs and outputs for components and pipelines**
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-inputs-outputs-pipeline?view=azureml-api-2
*Confidence: Verified (Feb 2026)*
*Confidence: Verified (April 2026)*
7. **Create jobs and input data for batch endpoints**
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-access-data-batch-endpoints-jobs?view=azureml-api-2
*Confidence: Verified (Feb 2026)*
*Confidence: Verified (April 2026)*
8. **Upgrade pipeline endpoints to SDK v2**
https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-deploy-pipelines?view=azureml-api-2
*Confidence: Verified (Feb 2026)*
*Confidence: Verified (April 2026)*
### Code Samples (Verified)
- **Azure ML Examples Repository (azureml-examples/sdk/python/schedules):**
https://github.com/Azure/azureml-examples
*Confidence: Verified (Feb 2026)*
*Confidence: Verified (April 2026)*
### Konfidensgradering per seksjon
@ -613,5 +635,5 @@ Er det >3 steg i workflow?
| Kostnad og lisensiering | Verified + Baseline | MS Learn: cost considerations + Azure pricing |
| For arkitekten | Baseline | Arkitekturkonsulent-erfaring |
**Verified:** Informasjon hentet direkte fra Microsoft Learn MCP-dokumentasjon (februar 2026).
**Verified:** Informasjon hentet direkte fra Microsoft Learn MCP-dokumentasjon (april 2026).
**Baseline:** Informasjon basert på modellkunnskap og arkitekturerfaring, konsistent med Azure ML prinsipper.

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@ -1,7 +1,6 @@
# CI/CD Pipelines for Machine Learning Models
**Last updated:** 2026-02
**Verified:** MCP 2026-04
**Last updated:** 2026-04
**Status:** GA
**Category:** MLOps & GenAIOps
@ -290,8 +289,10 @@ Disse signalene indikerer at din ML CI/CD ikke er production-ready:
### GitHub Actions Integration
### GitHub Actions with Azure Machine Learning (2026 Update)
The recommended authentication approach is **OpenID Connect (OIDC) with federated credentials** — eliminates long-lived secrets.
### GitHub Actions with Azure Machine Learning (Verified MCP 2026-04)
The recommended authentication approach is **OpenID Connect (OIDC) with federated credentials** — eliminates long-lived secrets. Two options:
- **Option 1: Microsoft Entra application** — Create app registration, configure federated identity credential, assign role.
- **Option 2: User-assigned managed identity** — Create UAI, configure federated identity credential, assign role.
**Workflow structure** (`/.github/workflows/`):
```yaml
@ -300,6 +301,7 @@ permissions:
jobs:
build:
steps:
- uses: actions/checkout@v4
- uses: azure/login@v2
with:
client-id: ${{ secrets.AZURE_CLIENT_ID }}
@ -311,11 +313,13 @@ jobs:
**MLOps v2 GitHub setup** (recommended end-to-end):
1. Fork `Azure/mlops-v2-gha-demo` template repo
2. Set GitHub secrets: `ARM_CLIENT_ID`, `ARM_CLIENT_SECRET`, `ARM_SUBSCRIPTION_ID`, `ARM_TENANT_ID`
3. Deploy infrastructure via `tf-gha-deploy-infra.yml` workflow
3. Deploy infrastructure via `tf-gha-deploy-infra.yml` workflow (Terraform)
4. Run `deploy-model-training-pipeline` and `deploy-online-endpoint-pipeline` workflows
**Pipeline stages**: Prepare Data → Train Model → Evaluate Model → Register Model → Deploy Endpoint
**Note (2026-04):** The `--json-auth`/`--sdk-auth` parameters for `az ad sp create-for-rbac` are deprecated. New projects should use OIDC with federated credentials instead.
**Setup:**
- Opprett `.github/workflows/` directory i repo
@ -666,7 +670,7 @@ CI/CD pipelines for ML krever compute for training og deployment:
1. **Use GitHub Actions with Azure Machine Learning**
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-github-actions-machine-learning
(Status: Verified 2026-02, fullstendig guide til GitHub Actions + Azure ML CLI v2)
(Status: Verified MCP 2026-04 — OIDC recommended; supports Entra app or user-assigned managed identity)
2. **MLOps and GenAIOps for AI workloads on Azure**
https://learn.microsoft.com/en-us/azure/well-architected/ai/mlops-genaiops
@ -674,7 +678,7 @@ CI/CD pipelines for ML krever compute for training og deployment:
3. **Set up MLOps with GitHub**
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-setup-mlops-github-azure-ml
(Status: Verified 2026-02, end-to-end MLOps setup med GitHub Actions)
(Status: Verified MCP 2026-04 — uses mlops-v2-gha-demo accelerator; --json-auth deprecated, OIDC recommended)
4. **How does Databricks support CI/CD for machine learning?**
https://learn.microsoft.com/en-us/azure/databricks/machine-learning/mlops/ci-cd-for-ml

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@ -1,6 +1,6 @@
# Data Drift Monitoring and Detection
**Last updated:** 2026-02
**Last updated:** 2026-04
**Verified:** MCP 2026-04
**Status:** GA
**Category:** MLOps & GenAIOps
@ -350,7 +350,7 @@ Hvis kunden bruker legacy `DataDriftDetector` (azureml-datadrift SDK):
## Kilder og verifisering
**Verified (Microsoft Learn MCP, 2026-02):**
**Verified (Microsoft Learn MCP, 2026-04):**
- Azure Machine Learning model monitoring concept: https://learn.microsoft.com/en-us/azure/machine-learning/concept-model-monitoring?view=azureml-api-2
- Monitor model performance in production: https://learn.microsoft.com/en-us/azure/machine-learning/how-to-monitor-model-performance?view=azureml-api-2
- Data drift (v1, deprecated): https://learn.microsoft.com/en-us/azure/machine-learning/how-to-monitor-datasets?view=azureml-api-1
@ -368,7 +368,7 @@ Hvis kunden bruker legacy `DataDriftDetector` (azureml-datadrift SDK):
**Unique Sources:** 12 Microsoft Learn URLs
### Azure ML Model Monitoring — Data Drift Detection (2026)
### Azure ML Model Monitoring — Data Drift Detection (2026) — Verified (MCP 2026-04)
**Model monitoring signals** (out-of-box for online endpoints):

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@ -2,6 +2,7 @@
**Kategori:** MLOps & GenAIOps
**Dato:** 2026-02-04
**Last updated:** 2026-04
**Confidence:** HIGH (basert på offisiell Microsoft-dokumentasjon)
**Verified:** MCP 2026-04
@ -711,7 +712,7 @@ mlflow.log_param("user_id_hash", user_id_hash) # Logged
**Primærkilder (Microsoft Learn):**
1. [MLflow for GenAI Apps and Agents - Continuous Improvement Cycle](https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/overview/)
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)
@ -720,10 +721,10 @@ mlflow.log_param("user_id_hash", user_id_hash) # Logged
**Code samples:**
- MLflow feedback logging: [Azure Databricks - Agent Framework](https://learn.microsoft.com/en-us/azure/databricks/generative-ai/agent-framework/non-conversational-agents#log-user-feedback)
- Model monitoring setup: [Azure ML - Monitor Model Performance](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-monitor-model-performance?view=azureml-api-2)
- GenAI evaluation: [MLflow 3.x - Evaluate App](https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/eval-monitor/evaluate-app)
- Model monitoring setup: [Azure ML - Monitor Model Performance](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-monitor-model-performance?view=azureml-api-2) (Verified MCP 2026-04 — supports data quality, data drift, prediction drift, feature attribution drift, and custom signals; integrates with Azure Event Grid for alerting)
- GenAI evaluation: [MLflow 3.x - Evaluate App](https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/eval-monitor/evaluate-app) (Verified MCP 2026-04 — tutorial covers RAG email app evaluation; new scorers: RetrievalGroundedness, Guidelines, RelevanceToQuery, Safety; version comparison with mlflow.genai.evaluate())
**Dato for siste verifikasjon:** 2026-02-04
**Dato for siste verifikasjon:** 2026-04-10
**MCP calls:** 6 (microsoft_docs_search: 3, microsoft_docs_fetch: 3, microsoft_code_sample_search: 2)
@ -742,7 +743,7 @@ Dette dokumentet dekker hele feedback loop-syklusen for både classical ML og Ge
Bruk arkitekturmønstrene til å visualisere løsningen for kunden. Påpek at MLflow Tracing + Agent Evaluation gir "free" observability (built-in i Databricks).
### MLflow 3 Evaluation & Feedback Loop (2026)
### MLflow 3 Evaluation & Feedback Loop (Verified MCP 2026-04)
MLflow 3 introduces a unified evaluation-monitoring lifecycle for GenAI feedback loops:
@ -753,19 +754,27 @@ MLflow 3 introduces a unified evaluation-monitoring lifecycle for GenAI feedback
4. **Gather human feedback** via Review App (expert annotations)
5. **Improve** prompts/models based on evaluation datasets
**Built-in LLM judges (scorers)**:
- `RetrievalGroundedness` — checks if response is grounded in retrieved data
- `RelevanceToQuery` — checks if response addresses the user request
- `Safety` — checks for harmful/inappropriate content
- `Guidelines(name, guidelines)` — custom policy/tone/style checks
- `Correctness` — factual correctness with expected_facts
**Azure ML Model Monitoring signals**:
- Data quality: null values, out-of-range, type mismatch
- Data drift: statistical distribution changes between training and production data
- Prediction drift: distribution shift in model outputs
- Feature attribution drift: changes in feature importance
- Custom signals: user-defined metrics via custom scripts
- Integrates with **Azure Event Grid** for alerting on threshold breaches
**Monitoring setup**:
```python
# Set up out-of-box monitoring for Azure ML online endpoints
# Monitors data drift, prediction drift automatically
# Integrates with Azure Event Grid for alerting
```
**Evaluation dataset workflow (new 2026-04)**:
1. Search production traces → select problematic + high-quality examples
2. Save to versioned eval dataset in Unity Catalog (`mlflow.genai.datasets.create_dataset()`)
3. Run evaluation harness with `mlflow.genai.evaluate(data=eval_dataset, predict_fn=..., scorers=...)`
4. Compare runs in UI (`Evaluation runs` view) or SDK (`mlflow.search_runs`)
5. Identify regressions per-metric before promoting new versions
**Continuous improvement cycle**: Production traces → MLflow evaluation datasets → Scorer alignment → Prompt/model update → A/B test → Production rollout

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@ -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)

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@ -1,7 +1,7 @@
# Inferencing Optimization and Caching
**Kategori:** MLOps & GenAIOps
**Dato:** 2026-02-04
**Dato:** 2026-04
**Forfattet av:** Cosmo Skyberg, Senior Microsoft AI Solution Architect
**Verified:** MCP 2026-04
@ -1015,7 +1015,7 @@ Diagnostikk:
**Confidence nivå: HIGH** — Denne referansen er basert på 12 MCP-kall til offisiell Microsoft-dokumentasjon og kodeeksempler.
### ONNX Inferencing Optimization for Computer Vision (Azure ML AutoML 2026)
### ONNX Inferencing Optimization for Computer Vision (Azure ML AutoML 2026) — Verified (MCP 2026-04)
ONNX (Open Neural Network Exchange) enables cross-framework interoperability and inference optimization:

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@ -1,6 +1,6 @@
# Infrastructure as Code for MLOps
**Dato:** 2026-02-04
**Dato:** 2026-04
**Kategori:** MLOps & GenAIOps
**Forfatter:** Cosmo Skyberg, Senior Microsoft AI Solution Architect
@ -572,7 +572,7 @@ terraform init && terraform apply
- Scan IaC repos for secrets (Microsoft Defender for Cloud: IaC vulnerability scanning)
- Immutable infrastructure preferred for business-critical workloads
**AI opportunity** (2026): AI tools (GitHub Copilot) can review IaC templates, identify misconfigurations, suggest security improvements, and generate templates from natural language.
**AI opportunity** (Verified MCP 2026-04): AI tools (GitHub Copilot) can review IaC templates for misconfigurations, suggest secure alternatives, and generate templates from natural language. Generative AI can analyze IaC templates and architectural diagrams, generate threat models, and recommend IaC updates from pull requests. Agent-based solutions can infer infrastructure needs from code and generate PRs with recommended IaC changes.
**MLOps v2 infrastructure**: `tf-gha-deploy-infra.yml` workflow in `Azure/mlops-v2-gha-demo` deploys full Azure ML infrastructure via Terraform + GitHub Actions.
@ -864,7 +864,7 @@ terraform {
## Kilder og verifisering
Denne kunnskapsreferansen er basert på følgende verifiserte kilder (hentet 2026-02-04):
Denne kunnskapsreferansen er basert på følgende verifiserte kilder (hentet 2026-04):
1. **Microsoft Learn - What is Infrastructure as Code (IaC)?**
- URL: https://learn.microsoft.com/devops/deliver/what-is-infrastructure-as-code
@ -921,7 +921,7 @@ Denne kunnskapsreferansen er basert på følgende verifiserte kilder (hentet 202
- **microsoft_docs_fetch calls:** 3
- **microsoft_code_sample_search calls:** 1
- **Total sources:** 10
- **Dato for research:** 2026-02-04
- **Dato for research:** 2026-04
**Confidence levels:**
- VERY_HIGH: Offisiell Microsoft-dokumentasjon, verifiserte code samples
@ -932,5 +932,5 @@ Alle kodeeksempler er hentet fra official Microsoft Learn eller GitHub repos und
---
**Oppdatert:** 2026-02-04
**Neste review:** 2026-05-04 (eller når Azure ML API major version oppdateres)
**Oppdatert:** 2026-04
**Neste review:** 2026-07-04 (eller når Azure ML API major version oppdateres)

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@ -1,7 +1,7 @@
# LLM Evaluation in Production Contexts
**Kategori:** MLOps & GenAIOps
**Sist oppdatert:** 2026-02-04
**Sist oppdatert:** 2026-04
**Confidence:** High (basert på offisiell Microsoft dokumentasjon, Azure AI Foundry SDK, og MLflow 3)
---
@ -575,15 +575,24 @@ MLflow 3 (SDK `mlflow[databricks]>=3.1`) introduces a unified evaluation model:
| Judge | Needs Ground Truth | Evaluates |
|-------|-------------------|-----------|
| `RelevanceToQuery` | No | Response relevance to user request |
| `RetrievalRelevance` | No | Retrieved context relevance to user request |
| `RetrievalGroundedness` | No | Hallucination detection |
| `Safety` | No | Harmful/toxic content |
| `Correctness` | Yes | Accuracy vs ground truth |
| `Completeness` | Yes | All questions addressed |
| `Fluency` | No | Grammatically correct and naturally flowing |
| `Equivalence` | Yes | Response equivalent to expected output |
| `RetrievalSufficiency` | Yes | Context provides all necessary information |
| `ToolCallCorrectness` | Yes | Tool calls and arguments |
| `ToolCallEfficiency` | No | Redundant tool usage |
| `Guidelines` | No | Custom natural-language rules |
| `ExpectationsGuidelines` | No (needs guidelines in expectations) | Per-example natural-language criteria |
**Multi-turn judges** (conversation-level): `ConversationCompleteness`, `UserFrustration`, `KnowledgeRetention`, `ConversationalSafety`
Verified (MCP 2026-04)
**Multi-turn judges** (conversation-level): `ConversationCompleteness`, `UserFrustration`, `KnowledgeRetention`, `ConversationalSafety`, `ConversationalGuidelines`, `ConversationalRoleAdherence`, `ConversationalToolCallEfficiency`
Verified (MCP 2026-04)
**Production monitoring**: Automatically runs scorers on production traces; uses Databricks-hosted LLM judges (EU workspaces: EU-hosted models). No prompts stored with Azure OpenAI (Abuse Monitoring opt-out).
@ -1088,7 +1097,7 @@ Production evaluation er ikke komplett uten human review loop. Anbefal:
- Power Platform evaluation gaps (product evolves rapidly)
- Human feedback loop implementation (no single canonical pattern)
**Ufullstendig informasjon (per feb 2026):**
**Ufullstendig informasjon (per april 2026):**
- Native Copilot Studio production evaluation features (roadmap item, not released)
- Detailed pricing for Azure AI Content Safety evaluators (bundled pricing, not per-call transparent)

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@ -1,6 +1,6 @@
# MLOps Fundamentals - Lifecycle and Principles
**Last updated:** 2026-02
**Last updated:** 2026-04
**Verified:** MCP 2026-04
**Status:** GA
**Category:** MLOps & GenAIOps
@ -296,7 +296,7 @@ jobs:
### DevOps-verktøy
### DevOps for Machine Learning — Azure DevOps Integration (2026)
### DevOps for Machine Learning — Azure DevOps Integration (Verified MCP 2026-04)
**Azure Pipelines + Azure ML** (how-to-devops-machine-learning):
@ -306,27 +306,36 @@ Automate the ML lifecycle via Azure DevOps pipelines:
3. Model deployment (public/private web service)
4. Monitoring (performance, data drift)
**Azure DevOps pipeline YAML pattern**:
**Prerequisite**: Python >=3.10 required for Azure ML SDK v2 scripts. Install [Azure Machine Learning extension for Azure Pipelines](https://marketplace.visualstudio.com/items?itemName=ms-air-aiagility.azureml-v2) from VS Marketplace.
**Authentication options** (Verified MCP 2026-04):
- **Azure Resource Manager service connection** (recommended) — use with `AzureMLJobWaitTask@1` from Azure ML extension
- **Generic service connection** — use with `InvokeRESTAPI` task calling REST API directly (api-version: `2024-04-01`)
**Azure DevOps pipeline YAML pattern** (ARM service connection):
```yaml
- task: AzureCLI@2
name: submit_azureml_job_task
inputs:
azureSubscription: $(service-connection)
scriptType: bash
inlineScript: |
job_name=$(az ml job create --file pipeline.yml -g $(resource-group) -w $(workspace) --query name -o tsv)
job_name=$(az ml job create --file pipeline.yml -g $(resource-group) -w $(workspace) --query name --output tsv)
echo "##vso[task.setvariable variable=JOB_NAME;isOutput=true;]$job_name"
- job: WaitForJobCompletion
pool: server # Server job — no agent costs
pool: server # Server job — no agent costs, runs on pipeline machine
dependsOn: SubmitAzureMLJob
steps:
- task: AzureMLJobWaitTask@1 # From Azure ML extension
- task: AzureMLJobWaitTask@1 # From Azure ML extension (not "classic")
inputs:
serviceConnection: $(service-connection)
azureMLJobName: $(azureml_job_name)
resourceGroupName: $(resource-group)
azureMLWorkspaceName: $(workspace)
azureMLJobName: $(azureml_job_name_from_submit_job)
```
**Authentication options**:
- Azure Resource Manager service connection (recommended with Azure ML extension)
- Generic service connection (uses InvokeRESTAPI task)
**Note**: `AzureMLJobWaitTask@1` runs as a server job (no agent pool costs). Max wait: 2 days (Azure DevOps hard limit). Use `AzureMLJobWaitTask@1`, not the legacy "Machine Learning (classic)" extension.
**MLOps maturity model**: Manual → Partial automation → Full CI/CD → Full MLOps with monitoring
@ -495,5 +504,5 @@ Er dette en POC?
### Sist verifisert
Alle kilder verifisert via `microsoft-learn` MCP-server **2026-02-04**.
Alle kilder verifisert via `microsoft-learn` MCP-server **2026-04**.
Azure ML dokumentasjon gjelder **API v2 (current)** med mindre annet er nevnt.

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@ -1,7 +1,7 @@
# Security and Access Control in MLOps
**Kategori:** MLOps & GenAIOps
**Last updated:** 2026-04 | Verified: MCP 2026-04
**Last updated:** 2026-04
**Dato:** 2026-04-10
**Confidence:** HIGH — Basert på offisiell Microsoft Learn dokumentasjon (8 MCP-oppslag, 16 kilder)
@ -738,9 +738,10 @@ AmlComputeClusterNodeEvent
6. [Configure a private endpoint for an Azure Machine Learning workspace](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-private-link?view=azureml-api-2)
7. [Secure an Azure Machine Learning workspace with virtual networks](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-workspace-vnet?view=azureml-api-2)
8. [Data encryption with Azure Machine Learning](https://learn.microsoft.com/en-us/azure/machine-learning/concept-data-encryption?view=azureml-api-2)
(Verified MCP 2026-04 — Key updates: Azure Data Lake Storage Gen1 retired 2024-02-29; Azure Database for PostgreSQL Single Server retired 2025-03-28; Azure Database for MySQL Single Server retired 2024-09-16. Use Gen2 / Flexible Server variants.)
**Sist verifisert:** 2026-02-04
**Neste review:** Q2 2026 (ved nye identity/network features i Azure ML)
**Sist verifisert:** 2026-04-10
**Neste review:** Q3 2026 (ved nye identity/network features i Azure ML)
---

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@ -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

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@ -5,7 +5,7 @@
**Målgruppe:** Arkitekter som planlegger ML-modellutplassering i produksjon
**Konfidensgrad:** ⚡️⚡️⚡️ Høy (basert på Microsoft Learn + offisielle code samples)
**Verified:** MCP 2026-04
**Last updated:** 2026-04
## Introduksjon
@ -620,6 +620,8 @@ deployment = ManagedOnlineDeployment(
- Built-in support for scikit-learn, TensorFlow, PyTorch
- Enklere rollback (bare endre model version)
**Auth note (Verified MCP 2026-04):** For production deployments, use Microsoft Entra token-based authentication (`aad_token`) instead of key-based auth — provides identity-based access control.
**Referanse:** [Deploy MLflow models to online endpoints](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-mlflow-models-online-endpoints?view=azureml-api-2)
---
@ -1020,7 +1022,7 @@ Denne kunnskapsreferansen er basert på følgende Microsoft Learn-artikler og co
**Primære kilder:**
1. [Perform safe rollout of new deployments for real-time inference](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-safely-rollout-online-endpoints?view=azureml-api-2)
→ Komplett guide til blue-green deployment og traffic mirroring
→ Komplett guide til blue-green deployment og traffic mirroring (Verified MCP 2026-04)
2. [MLOps model management with Azure Machine Learning](https://learn.microsoft.com/en-us/azure/machine-learning/concept-model-management-and-deployment?view=azureml-api-2)
→ Oversikt over deployment capabilities og controlled rollout
@ -1032,7 +1034,7 @@ Denne kunnskapsreferansen er basert på følgende Microsoft Learn-artikler og co
→ Canary deployment med Azure DevOps Pipelines
5. [Progressive rollout of MLflow models to Online Endpoints](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-mlflow-models-online-progressive?view=azureml-api-2)
→ MLflow-spesifikk progressive rollout
→ MLflow-spesifikk progressive rollout; supports model packaging (--with-package) for endpoints without egress connectivity (Verified MCP 2026-04)
**Code samples:**
- [azureml-examples/sdk/python/endpoints/online/managed/online-endpoints-safe-rollout.ipynb](https://github.com/Azure/azureml-examples/blob/main/sdk/python/endpoints/online/managed/online-endpoints-safe-rollout.ipynb)
@ -1040,7 +1042,7 @@ Denne kunnskapsreferansen er basert på følgende Microsoft Learn-artikler og co
**Well-Architected Framework:**
- [Architecture strategies for safe deployment practices](https://learn.microsoft.com/en-us/azure/well-architected/operational-excellence/safe-deployments)
→ Progressive exposure model, bake times, rollback strategies
→ Progressive exposure model, bake times, rollback strategies (Verified MCP 2026-04 — adds safe decommissioning guidance + AI opportunity note for GenAI-assisted rollout tuning)
**Pricing (sist verifisert: 2026-02-04):**
- [Azure Machine Learning pricing](https://azure.microsoft.com/en-us/pricing/details/machine-learning/)
@ -1050,8 +1052,8 @@ Denne kunnskapsreferansen er basert på følgende Microsoft Learn-artikler og co
---
**Sist oppdatert:** 2026-02-04
**Neste review:** 2026-05-04 (eller ved større endringer i Azure ML deployment capabilities)
**Sist oppdatert:** 2026-04-10
**Neste review:** 2026-07-10 (eller ved større endringer i Azure ML deployment capabilities)
### Safe Rollout / Blue-Green Deployment (Azure Well-Architected 2026)
@ -1085,3 +1087,7 @@ az ml online-endpoint update --name my-endpoint --traffic blue=90 green=10
**Emergency SDP**: Prescriptive protocols for hotfix acceleration — approval stage and bake time reduction — with explicit approval criteria.
**Safe decommissioning (new in 2026-04)**: Removing components is highest-risk. Steps: validate inactivity → preserve state (backup/export) → disable before deleting → monitor watch window covering full usage cycle → clean up residual references. Skip disable only if compliance requires immediate removal.
**AI opportunity**: AI can assist rollout tuning — analyze deployment docs, code reviews, incident history to suggest rollout strategies and parameters (low/medium GenAI approach). Advanced agentic solutions can auto-update rollout configurations.

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@ -1,6 +1,6 @@
# Model Drift and Performance Degradation Detection
**Last updated:** 2026-02
**Last updated:** 2026-04
**Verified:** MCP 2026-04
**Status:** GA
**Category:** MLOps & GenAIOps
@ -41,7 +41,7 @@ Azure Machine Learning støtter flere built-in signals (med GA- eller preview-st
| **Feature Attribution Drift** | Feature importance-endringer | Preview | Normalized Discounted Cumulative Gain |
| **Model Performance** | Objektiv ytelse (krever ground truth) | Preview | Accuracy, Precision, Recall (classification); MAE, MSE, RMSE (regression) |
**Verified (MCP):** Metrics og signal-typer hentet fra offisiell Microsoft Learn-dokumentasjon (2026-02).
**Verified (MCP):** Metrics og signal-typer hentet fra offisiell Microsoft Learn-dokumentasjon (2026-04).
### 2. Reference Data
@ -582,44 +582,44 @@ Email Alerts + Azure Monitor Dashboard
1. **Azure Machine Learning model monitoring (Concept)**
https://learn.microsoft.com/en-us/azure/machine-learning/concept-model-monitoring?view=azureml-api-2
*Verified: 2026-02 via microsoft_docs_fetch*
*Verified: 2026-04 via microsoft_docs_fetch*
- Monitoring signals, metrics, reference data, lookback windows
2. **Monitor the performance of models deployed to production**
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-monitor-model-performance?view=azureml-api-2
*Verified: 2026-02 via microsoft_docs_fetch*
*Verified: 2026-04 via microsoft_docs_fetch*
- Setup guides (CLI, SDK, Studio), Event Grid integration, interpret results
3. **Data drift (preview) will be retired, and replaced by Model Monitor**
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-monitor-datasets?view=azureml-api-1
*Verified: 2026-02 via microsoft_docs_search (3 results)*
*Verified: 2026-04 via microsoft_docs_search (3 results)*
- Legacy DataDriftDetector (v1) vs. Model Monitor (v2)
4. **Trigger applications, processes, or CI/CD workflows based on Azure Machine Learning events**
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-event-grid?view=azureml-api-2
*Verified: 2026-02 via microsoft_docs_search*
*Verified: 2026-04 via microsoft_docs_search*
- Event Grid integration, advanced filters
5. **Machine learning operations (MLOps v2)**
https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/machine-learning-operations-v2
*Verified: 2026-02 via microsoft_docs_search (multiple references)*
*Verified: 2026-04 via microsoft_docs_search (multiple references)*
- Data drift, prediction drift, resource monitoring
### Code Samples (MCP-verified)
1. **Model monitoring setup (Python SDK v2)**
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-monitor-model-performance
*Verified: 2026-02 via microsoft_code_sample_search*
*Verified: 2026-04 via microsoft_code_sample_search*
- Out-of-box monitoring, advanced monitoring, model performance
2. **DataDriftDetector (Python SDK v1 – deprecated)**
https://learn.microsoft.com/en-us/python/api/azureml-datadrift/azureml.datadrift.datadriftdetector
*Verified: 2026-02 via microsoft_code_sample_search*
*Verified: 2026-04 via microsoft_code_sample_search*
- Legacy API for comparison
3. **Custom signal component examples**
https://github.com/Azure/azureml-examples/tree/main/cli/monitoring/components/custom_signal
*Referenced: 2026-02 in Microsoft Learn documentation*
*Referenced: 2026-04 in Microsoft Learn documentation*
### Confidence Markers
@ -635,7 +635,7 @@ Email Alerts + Azure Monitor Dashboard
### Sist oppdatert
**2026-02** – Basert på Microsoft Learn-dokumentasjon (azure-ai-ml SDK v2, API version 2).
**2026-04** – Basert på Microsoft Learn-dokumentasjon (azure-ai-ml SDK v2, API version 2).
### Azure ML Model Drift & Performance Degradation Monitoring (2026)

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@ -1,6 +1,6 @@
# Model Evaluation Frameworks and Metrics
**Last updated:** 2026-02
**Last updated:** 2026-04
**Verified:** MCP 2026-04
**Status:** GA
**Category:** MLOps & GenAIOps
@ -119,10 +119,11 @@ MLflow 3 provides the evaluation framework for both traditional ML and GenAI app
| Type | Customization | Use Case |
|------|--------------|---------|
| Built-in judges | Minimal | Quick evaluation: `Correctness`, `RetrievalGroundedness`, `Safety` |
| Guidelines judges | Moderate | Custom natural-language rules (pass/fail) |
| Built-in judges | Minimal | Quick evaluation: `Correctness`, `RetrievalGroundedness`, `Safety`, `RelevanceToQuery`, `Fluency`, `Equivalence` — Verified (MCP 2026-04) |
| Guidelines judges | Moderate | Custom natural-language rules (pass/fail): `Guidelines`, `ExpectationsGuidelines` |
| Custom LLM judges | Full | Domain-specific criteria, detailed scoring |
| Code-based scorers | Full | Deterministic: exact match, format validation, business logic |
| Multi-turn judges | Minimal | Conversation-level: `ConversationCompleteness`, `UserFrustration`, `KnowledgeRetention`, `ConversationalSafety` — Verified (MCP 2026-04) |
**Key evaluation functions**:
```python

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@ -1,6 +1,6 @@
# Model Versioning and Registry Management
**Last updated:** 2026-02
**Last updated:** 2026-04
**Verified:** MCP 2026-04
**Status:** GA
**Category:** MLOps & GenAIOps
@ -306,6 +306,8 @@ Azure AI Foundry Model Catalog bruker samme underliggende registry-infrastruktur
### Power Platform AI
**Scenario:** Registrer Custom AI Builder model i Azure ML Registry for reuse.
> **Merk (Verified MCP 2026-04):** For production online endpoint deployments anbefaler Microsoft nå Microsoft Entra token-based authentication (`aad_token`) fremfor key-based authentication for forbedret sikkerhet via identity-based access control.
- Tren modell i AI Builder
- Eksporter modell (hvis tilgjengelig)
- Registrer i Azure ML Registry som MLflow model
@ -511,7 +513,7 @@ az ml model list --registry-name my-registry --query "[?created<'$cutoff_date'].
## Kilder og verifisering
### Microsoft Learn (Verified via MCP research, February 2026)
### Microsoft Learn (Verified via MCP research, April 2026)
1. **Share models, components, and environments across workspaces with registries**
- URL: https://learn.microsoft.com/en-us/azure/machine-learning/how-to-share-models-pipelines-across-workspaces-with-registries?view=azureml-api-2
@ -566,7 +568,7 @@ az ml model list --registry-name my-registry --query "[?created<'$cutoff_date'].
- **Document fetches:** 2 (Full registry guide, MLflow management guide)
- **Code samples:** 1 (MLflow Python SDK examples)
- **Unique sources:** 7 Microsoft Learn articles
- **Research timestamp:** February 2026
- **Research timestamp:** April 2026
---

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@ -1,7 +1,7 @@
# Monitoring and Observability for ML Systems
**Kategori:** MLOps & GenAIOps
**Dato:** 2026-02-04
**Dato:** 2026-04
**Kilder:** Microsoft Learn (azure-machine-learning, azure-monitor)
**Konfidensgrad:** ⭐⭐⭐⭐⭐ (Verifisert mot offisiell Microsoft-dokumentasjon)
@ -300,7 +300,7 @@ create_monitor:
### Azure Monitor
### Azure Machine Learning Monitoring Architecture (2026)
### Azure Machine Learning Monitoring Architecture (2026) — Verified (MCP 2026-04)
**Azure Monitor integration**:
- All metrics in namespace: `Machine Learning Service Workspace`
@ -630,7 +630,7 @@ Azure Machine Learning Model Monitoring gir production-ready overvåkning av ML-
## Kilder og verifisering
**Primærkilder** (✅ Verifisert 2026-02-04):
**Primærkilder** (✅ Verifisert 2026-04):
1. [Monitor the performance of models deployed to production](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-monitor-model-performance?view=azureml-api-2)
2. [Azure Machine Learning model monitoring](https://learn.microsoft.com/en-us/azure/machine-learning/concept-model-monitoring?view=azureml-api-2)
3. [Detect and mitigate potential issues using AIOps and machine learning in Azure Monitor](https://learn.microsoft.com/en-us/azure/azure-monitor/aiops/aiops-machine-learning)
@ -646,5 +646,5 @@ Azure Machine Learning Model Monitoring gir production-ready overvåkning av ML-
- ⭐⭐⭐⭐ = Basert på Microsoft Learn, men med noe tolkning
- ⭐⭐⭐ = Community best practices (ikke offisiell Microsoft-guidance)
**Sist verifisert:** 2026-02-04
**Sist verifisert:** 2026-04
**Neste review:** Når Azure ML Model Monitoring v3 lanseres (roadmap Q2 2026)

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@ -62,11 +62,12 @@ Eskaleringsrutiner må reflektere organisasjonens modenhetsnivå. En Minimum Via
| **Azure Function** | Custom logic (e.g., invoke model rollback API) | HTTP trigger med access key | ❌ No | ❌ No |
| **Webhook** | Invoke external incident mgmt (PagerDuty, ServiceNow) | Basic auth via URI eller secure webhook (Entra ID) | ❌ No | ✅ Yes (limited) |
| **Event Hub** | Stream til SIEM (Microsoft Sentinel) for correlation | Managed Identity (Event Hubs Data Sender, Role ID: 2b629674) | ✅ Yes | ✅ Yes (up to API 2023-09-01-preview) |
| **Secure Webhook** | Invoke protected API med Entra ID-auth | Microsoft Entra app registration | ❌ No | ✅ Yes |
| **ITSM Connector** | Create incidents i ServiceNow, Cherwell | ITSM connection credentials | ❌ No | ❌ No |
*(Verified MCP 2026-04)*
**Managed Identity Best Practice:** For Automation Runbooks, Logic Apps og Event Hubs, bruk managed identity i stedet for service principals. Azure Function og Webhook støtter ikke managed identity — bruk HTTP trigger access key respektive secure webhook med Entra ID. Azure Portal legger automatisk til role assignments ved konfigurasjon. For PowerShell/CLI/SDK må du manuelt tildele roller (se tabell over). *(Verified MCP 2026-04)*
**Managed Identity Best Practice (preview):** Managed Identity-støtte for Action Groups er nå tilgjengelig i **preview**. For Automation Runbooks, Logic Apps og Event Hubs, bruk managed identity i stedet for service principals. Azure Function, Webhook, Secure Webhook og ITSM støtter ikke managed identity — bruk HTTP trigger access key respektive secure webhook med Entra ID. Azure Portal legger automatisk til role assignments ved konfigurasjon. For PowerShell/CLI/SDK må du manuelt tildele roller (se tabell over). *(Verified MCP 2026-04)*
---

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@ -88,7 +88,7 @@ Workbooks kan deployes via ARM templates for consistency across teams:
"name": "ai-operations-workbook",
"type": "microsoft.insights/workbooks",
"location": "[resourceGroup().location]",
"apiVersion": "2022-04-01", // For workbook instances; workbook templates bruker 2020-11-20 (workbooktemplates resource type). Bicep støttes nå offisielt som alternativ til ARM JSON. *(Verified MCP 2026-04)*
"apiVersion": "2018-06-17-preview", // For workbook instances; workbook templates bruker 2019-10-17-preview (workbooktemplates resource type). Bicep støttes nå offisielt som alternativ til ARM JSON. *(Verified MCP 2026-04)*
"properties": {
"displayName": "AI Operations Dashboard",
"serializedData": "{\"version\":\"Notebook/1.0\",\"items\":[...]}",
@ -488,7 +488,7 @@ Når kunden spør om dashboards for AI operations:
- [Power BI + Azure Monitor](https://learn.microsoft.com/en-us/azure/azure-monitor/logs/log-powerbi)
### Code Samples
- [Workbook ARM/Bicep template samples](https://learn.microsoft.com/en-us/azure/azure-monitor/visualize/workbooks-samples) — inkluderer nå Bicep-syntaks; workbooktemplates apiVersion er `2020-11-20` *(Verified MCP 2026-04)*
- [Workbook ARM/Bicep template samples](https://learn.microsoft.com/en-us/azure/azure-monitor/visualize/workbooks-samples) — workbook templates bruker apiVersion `2019-10-17-preview` (type: microsoft.insights/workbooktemplates); workbook instances bruker `2018-06-17-preview` (type: Microsoft.Insights/workbooks) *(Verified MCP 2026-04)*
- [Azure AI Foundry Grafana dashboard ID: 24039](https://grafana.com/grafana/dashboards/24039)
- [KQL query examples for AI monitoring](https://learn.microsoft.com/en-us/azure/data-explorer/kusto/query/samples)

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@ -1,6 +1,6 @@
# Monitoring and Alerting for Failover Detection
**Last updated:** 2026-02
**Last updated:** 2026-04
**Status:** GA
**Category:** Business Continuity & Disaster Recovery
@ -427,22 +427,22 @@ Azure Monitor Application Insights tilbyr nå dedikert støtte for AI-agenter vi
| **Live metrics** | Sanntids health under failover-scenarier |
| **Availability tests** | Automatisk helsesjekk av agent-endepunkter |
### Instrumenteringsveiledning per agent-plattform
### Instrumenteringsveiledning per agent-plattform (Verified MCP 2026-04)
- **Azure AI Foundry-agenter:** Koble Application Insights til Foundry-prosjektet for automatisk tracing
- **Copilot Studio-agenter:** Konfigurer built-in telemetri-eksport til App Insights
- **Microsoft Agent Framework (self-hosted):** Bruk Azure Monitor OpenTelemetry Distro
- **LangChain/LangGraph og OpenAI Agents SDK:** Bruk Azure AI OpenTelemetry Tracer
- **Azure AI Foundry-agenter:** Start med [tracing setup i Foundry](https://learn.microsoft.com/azure/foundry/observability/how-to/trace-agent-setup). Koble Application Insights til Foundry-prosjektet for automatisk tracing. Kan også bruke Azure Monitor OpenTelemetry Distro med Foundry SDK.
- **Copilot Studio-agenter:** Konfigurer built-in telemetri-eksport til App Insights via innstillinger i Copilot Studio.
- **Microsoft Agent Framework (self-hosted):** Bruk Azure Monitor OpenTelemetry Distro for telemetri til Azure Monitor.
- **LangChain/LangGraph og OpenAI Agents SDK:** Bruk Azure AI OpenTelemetry Tracer. Framework-spesifikk veiledning tilgjengelig i Foundry docs.
**Anbefaling:** Gi hver agent et unikt navn for å skille dem i Agent details view. Bruk samme App Insights-ressurs for agenter som er del av et større system.
**Anbefaling:** Gi hver agent et unikt navn for å skille dem i Agent details view. Bruk samme App Insights-ressurs for agenter som er del av et større system. Vil du se agenter i Azure AI Foundry i tillegg til Azure Monitor, [koble App Insights-ressursen til Foundry-prosjektet](https://learn.microsoft.com/azure/foundry/observability/how-to/trace-agent-setup#connect-application-insights-to-your-foundry-project).
## Referanser
- [Monitor Azure OpenAI](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/monitor-openai) — OpenAI monitoring og alerting
- [Monitor Azure AI Search](https://learn.microsoft.com/en-us/azure/search/monitor-azure-cognitive-search) — AI Search monitoring
- [Azure Monitor alerts overview](https://learn.microsoft.com/en-us/azure/azure-monitor/alerts/alerts-overview) — Alert-rammeverk *(Verified MCP 2026-04)* — Stateful vs. stateless alerts, Simple Log Search Alerts (preview) for per-row evaluering, Query-based metric alerts for Prometheus/OTel (public preview). Alert processing rules for suppression ved planlagt vedlikehold. Opptil 5 action groups per alert rule.
- [Azure Monitor alerts overview](https://learn.microsoft.com/en-us/azure/azure-monitor/alerts/alerts-overview) — Alert-rammeverk *(Verified MCP 2026-04)* — Stateful vs. stateless alerts. **Simple Log Search Alerts** (GA) for per-row KQL evaluering — raskere varsling enn tradisjonelle log alerts. **Query-based metric alerts** for Prometheus/OTel (public preview). Alerts stored 30 dager. Fired instances er read-only. Alert processing rules for suppression ved planlagt vedlikehold. **Azure Monitor Baseline Alerts** (`aka.ms/amba`) for policy-basert alerting i skala via Azure Policy.
- [Health modeling and observability of mission-critical workloads](https://learn.microsoft.com/en-us/azure/well-architected/mission-critical/mission-critical-health-modeling) — Health modeling
- [Application Insights overview](https://learn.microsoft.com/en-us/azure/azure-monitor/app/app-insights-overview) — APM for applikasjoner *(Verified MCP 2026-04)* — Nå OpenTelemetry-basert (OTel) som primær instrumentering. Nye features: **Agent details view** for AI-agenter fra Foundry, Copilot Studio og tredjeparts agenter. Støtter: Azure AI Foundry (via Foundry SDK tracing), Copilot Studio (built-in telemetri → App Insights), Microsoft Agent Framework (self-hosted), LangChain/LangGraph og OpenAI Agents SDK. Batch og continuous evaluations for produksjonstraffic. Live Metrics for sanntids observabilitet under failover-scenarier.
- [Application Insights overview](https://learn.microsoft.com/en-us/azure/azure-monitor/app/app-insights-overview) — APM for applikasjoner *(Verified MCP 2026-04)* — OpenTelemetry (OTel) er primær instrumentering. AI-agenter støttes via Agents-tab i getting started. Azure Functions støtter OTel via `"telemetryMode": "OpenTelemetry"` i `host.json`. Nye views: **Agent details view** (Foundry, Copilot Studio, tredjeparts), **SDK Stats** (exporter success/drop metrics), **Dashboards with Grafana** (direkte i Azure portal). Evaluations: batch (local/cloud/portal) og continuous (produksjonstraffic). Classic API SDKs migreres til OTel — se migrasjonsveiledning. Fired alert instances er nå read-only (kan ikke editeres etter at de er trigget).
- [Azure Service Health](https://learn.microsoft.com/en-us/azure/service-health/overview) — Azure-tjenestestatus
## For Cosmo

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@ -166,7 +166,7 @@ builder.Services.AddApplicationInsightsTelemetry(new ApplicationInsightsServiceO
| **Query-frekvens** | Daglig/ukentlig | Månedlig/ved incidents | Sjelden (search jobs) |
| **Query-kompleksitet** | Full KQL, joins, aggregeringer | Begrenset KQL (8 dager) | Search jobs kun |
| **Ingestion-volum** | Moderat | Høyt (debugging) | Veldig høyt (verbose) |
| **Alerts** | Støttes | Støttes ikke | Støttes ikke |
| **Alerts** | Støttes | ✅ (Simple Log Alerts) — Verified (MCP 2026-04) | Støttes ikke |
| **Retention** | 30-730 dager | 8 dager interactive + long-term | Long-term kun |
| **Pris (ingestion)** | Standard | ~50% lavere | ~75% lavere |
| **Workspace replication** | ✅ | ✅ | ❌ (data ikke replikert — ingen beskyttelse ved regional feil) |
@ -174,8 +174,8 @@ builder.Services.AddApplicationInsightsTelemetry(new ApplicationInsightsServiceO
**Beslutningstre:**
1. **Trenger du real-time alerting?** → Analytics
2. **Queries kun ved feilsøking?** → Basic
3. **Kun compliance-arkivering?** → Auxiliary
2. **Queries kun ved feilsøking?** → Basic (støtter Simple Log Alerts — Verified MCP 2026-04)
3. **Kun compliance-arkivering?** → Auxiliary (støtter Microsoft Sentinel og Search jobs — Verified MCP 2026-04)
### Vanlige feil
@ -443,7 +443,7 @@ For volumer >1 TB/dag, vurder dedicated cluster for ytterligere besparelser (clu
10. **Azure Monitor Logs overview: Table plans:**
https://learn.microsoft.com/en-us/azure/azure-monitor/logs/data-platform-logs#table-plans
*Confidence: Verified (MCP 2026-04)* – Analytics, Basic, Auxiliary table plans. Oppdatering 2026-04: Auxiliary-plan bekrefter ingen workspace replication (data ikke beskyttet mot regional feil) og ingen Customer Lockbox-støtte.
*Confidence: Verified (MCP 2026-04)* – Analytics, Basic, Auxiliary table plans. Oppdatering 2026-04: Basic-plan støtter nå Simple Log Alerts (✅), ikke kun Analytics-plan. Auxiliary-plan bekrefter ingen workspace replication (data ikke beskyttet mot regional feil) og ingen Customer Lockbox-støtte. Auxiliary-plan støtter Microsoft Sentinel (✅), Search jobs (✅) og Summary rules (✅). Verified (MCP 2026-04)
### Norsk lovverk (Baseline-kunnskap)

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@ -389,7 +389,7 @@ Connection pooling har spesielle hensyn for norsk offentlig sektor:
- [Guidelines for using HttpClient](https://learn.microsoft.com/dotnet/fundamentals/networking/http/httpclient-guidelines) — HttpClient best practices
- [Pool HTTP connections with HttpClientFactory](https://learn.microsoft.com/aspnet/core/performance/performance-best-practices) — ASP.NET performance
- [Manage connections in Azure Functions](https://learn.microsoft.com/azure/azure-functions/manage-connections) — Serverless connection management
- [Use a gateway in front of multiple Azure OpenAI deployments](https://learn.microsoft.com/azure/architecture/ai-ml/guide/azure-openai-gateway-multi-backend) — Multi-backend gateway patterns
- [Use a gateway in front of multiple Azure OpenAI deployments or instances](https://learn.microsoft.com/azure/architecture/ai-ml/guide/azure-openai-gateway-multi-backend) — Multi-backend gateway patterns (Azure OpenAI i Foundry Models) — Verified (MCP 2026-04)
## For Cosmo

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@ -405,8 +405,10 @@ Microsoft dokumenterer multi-backend gateway som den anbefalte arkitekturmønste
### Anbefalte topologier for rate limit-distribusjon
> **Viktig:** Standard-kvote er subscription-nivå, ikke Azure OpenAI-instansnivå. Load balancing mellom standard-instanser i samme subscription gir IKKE høyere gjennomstrømning — bruk separate subscriptions eller global/data zone deployments for reell kvoteutvidelse. — Verified (MCP 2026-04)
| Topologi | Kvote-kapasitet | Kompleksitet | Anbefalt for |
|----------|----------------|--------------|--------------|
|----------|----------------|--------------|------------|
| Single instance | Baseline TPM | Lav | Utvikling, lav trafikk |
| Multi-backend, single region | 2-5x baseline | Medium | Produksjon, standard |
| Multi-subscription | 5-20x baseline | Høy | Høy trafikk enterprise |
@ -475,7 +477,7 @@ Microsoft dokumenterer multi-backend gateway som den anbefalte arkitekturmønste
- [Manage Azure OpenAI quota](https://learn.microsoft.com/azure/ai-foundry/openai/how-to/quota) — Kvotehåndtering
- [Azure OpenAI quotas and limits](https://learn.microsoft.com/azure/ai-foundry/openai/quotas-limits) — Grenser per modell
- [Azure OpenAI SDK retry handling](https://learn.microsoft.com/azure/ai-foundry/openai/supported-languages) — SDK retry-konfigurasjon
- [Use a gateway in front of Azure OpenAI](https://learn.microsoft.com/azure/architecture/ai-ml/guide/azure-openai-gateway-multi-backend) — Multi-region gateway
- [Use a gateway in front of multiple Azure OpenAI deployments or instances](https://learn.microsoft.com/azure/architecture/ai-ml/guide/azure-openai-gateway-multi-backend) — Multi-region gateway (Azure OpenAI i Foundry Models) — Verified (MCP 2026-04)
## For Cosmo

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@ -28,6 +28,8 @@ Latensforskjellen mellom regioner kan være betydelig: en forespørsel fra Oslo
### Deployment-typer og regionvalg
> **Anbefaling (Verified MCP 2026-04):** Hvis du ikke trenger å begrense databehandling til én bestemt region, bruk **Global** eller **Data Zone**-deployments for å utnytte Azures globale infrastruktur til dynamisk ruting til datasentre med ledig kapasitet — fremfor å bygge kompleks multi-region gateway-logikk.
| Deployment Type | Data Location | Routing | Bruksområde |
|----------------|---------------|---------|-------------|
| Global Standard | Any Azure region | Automatisk til ledig kapasitet | Høyest tilgjengelighet, lavest kostnad |
@ -394,7 +396,7 @@ Microsoft dokumenterer nå fire formelle topologier for Azure OpenAI gateway:
## Referanser
- [Use a gateway for multi-backend Azure OpenAI](https://learn.microsoft.com/azure/architecture/ai-ml/guide/azure-openai-gateway-multi-backend) — Multi-region patterns
- [Use a gateway in front of multiple Azure OpenAI deployments or instances](https://learn.microsoft.com/azure/architecture/ai-ml/guide/azure-openai-gateway-multi-backend) — Multi-region patterns (Azure OpenAI i Foundry Models) — Verified (MCP 2026-04)
- [Azure Front Door](https://learn.microsoft.com/azure/frontdoor/front-door-overview) — Global load balancing
- [APIM multi-region deployment](https://learn.microsoft.com/azure/api-management/api-management-howto-deploy-multi-region) — Regional gateway
- [Azure OpenAI deployment types](https://learn.microsoft.com/azure/ai-foundry/openai/how-to/deployment-types) — Global vs Regional