open/ktg-plugin-marketplace
1
0
Fork 0
Code Issues Pull requests Releases Wiki Activity Actions

docs(architect): weekly KB update — 106 files refreshed (2026-04)

Browse source 
Updates across all 5 skills: ms-ai-advisor, ms-ai-engineering,
ms-ai-governance, ms-ai-security, ms-ai-infrastructure.

Key changes:
- Language Services (Custom Text Classification, Text Analytics, QnA):
  retirement warning 2029-03-31, migration guides to Foundry/GPT-4o
- Agentic Retrieval: 50M free reasoning tokens/month (Public Preview)
- Computer Use: Claude Sonnet 4.5 (preview) + OpenAI CUA models
- Agent Registry: Risks column (M365 E7), user-shared/org-published types
- Declarative agents: schema v1.5 → v1.6, Store validation requirements
- MLflow 3: 13 built-in LLM judges, production monitoring, Genie Code
- AG-UI HITL: ApprovalRequiredAIFunction (C#) + @tool(approval_mode) (Python)
- Entra ID Ignite 2025: Agent ID Admin/Developer RBAC roles, Conditional Access
- Security Copilot: 400 SCU/month per 1000 M365 E5 licenses, auto-provisioned
- Fast Transcription API: phrase lists, 14-language multi-lingual transcription
- Azure Monitor Workbooks: Bicep support, RBAC specifics
- Power Platform Copilot: data residency (Norway/Europe → EU DB, Bing → USA)
- RAG security-rbac: 4-approach table (GA + 3 preview access control methods)
- IaC MLOps: Well-Architected OE:05 principles, Bicep/Terraform patterns
- Translator: image file batch translation Preview (JPEG/PNG/BMP/WebP)

All 106 files: Last updated 2026-04 | Verified: MCP 2026-04

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Kjell Tore Guttormsen 2026-04-10 09:13:24 +02:00
commit ff6a50d14f
104 changed files with 1986 additions and 520 deletions
Download patch file Download diff file Expand all files Collapse all files

13
plugins/ms-ai-architect/skills/ms-ai-engineering/references/agent-orchestration/agent-365-governance-and-deployment.md
View file

@ -22,12 +22,13 @@ Agent Registry er det sentrale administrasjonspunktet for alle agenter i organis
| Komponent | Beskrivelse | Tilgang |
|-----------|-------------|---------|
| **Agent Inventory** | Full oversikt over Microsoft-bygde, partner-bygde og interne agenter | AI Admin, Global Admin, Global Reader (view-only) |
| **Agent Details** | Metadata (capabilities, data sources, actions, sensitivity labels) | Per agent-basis |
| **Agent Inventory** | Full oversikt over Microsoft-bygde, partner-bygde, user-shared og org-published agenter | AI Admin, Global Admin, Global Reader (view-only) |
| **Agent Details** | Metadata (capabilities, data sources, actions, sensitivity labels, permissions-tab) | Per agent-basis |
| **Security & Compliance** | Oversikt over sikkerhetsrisiko (Entra alerts), compliance gaps (Purview) | Integrert med Defender/Purview |
| **Ownerless Agent Management** | Identifisering av agenter uten aktiv eier (f.eks. etter at bruker er slettet) | Real-time oppdatering |
| **Ownerless Agent Management** | Identifisering av agenter uten aktiv eier. Dashboard viser total count, one-click filter, og real-time updates ved brukersletting. *(Verified MCP 2026-04)* | Real-time oppdatering |
| **Risks Column** | Aggregerte high-severity risks fra Entra, Defender og Purview per agent. Kun tilgjengelig med **Microsoft 365 E7-lisens**. *(Verified MCP 2026-04)* | AI Admin, Global Reader |
**Verified (Microsoft Learn, 2026-02)**
**Verified (Microsoft Learn, 2026-04)**
### Agent Lifecycle Actions
@ -38,7 +39,7 @@ Administratorer har 11 lifecycle management actions tilgjengelig i Admin Center:
| **Publish** | Gjør agent tilgjengelig for installasjon (krever AI Admin approval) | Kontrollert utrulling til spesifikke grupper |
| **Activate** | Tillater brukere å installere agenten og opprette instanser | Selvbetjent agent-onboarding |
| **Deploy** | Automatisk installasjon for brukere (ready-to-use) | Zero-touch deployment |
| **Pin** | Fremhev agent i Copilot-interface (opptil 3 administrator-pinned agents) | Prioritering av business-kritiske agenter |
| **Pin** | Fremhev agent i Copilot-interface (opptil 3 administrator-pinned agents per tenant; kun deployed agents kan pinnes; Researcher/Analyst kan ikke scopes — bruk Block for disse) *(Verified MCP 2026-04)* | Prioritering av business-kritiske agenter |
| **Block** | Sperr tilgang for hele organisasjonen | Akutt sikkerhetsrespons |
| **Remove** | Fjern fra tenant inventory (kan gjenopprettes fra store) | Midlertidig deaktivering |
| **Delete** | Permanent sletting (inkludert SharePoint Embedded containers) | Irreversibel cleanup (24t propagation) |
@ -353,7 +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**
- [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)
- [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**

31
plugins/ms-ai-architect/skills/ms-ai-engineering/references/agent-orchestration/agent-evaluation-testing-frameworks.md
View file

@ -1,6 +1,6 @@
# Agent Evaluation and Testing Frameworks
**Last updated:** 2026-02
**Last updated:** 2026-04 | Verified: MCP 2026-04
**Status:** GA (Azure AI Evaluation SDK), Preview (Agent-specific evaluators)
**Category:** Agent Orchestration & Automation
@ -276,6 +276,33 @@ evaluator = TaskAdherenceEvaluator(
- **KQL queries:** Flexible querying av evaluation metrics over tid
- **Alerts:** Sett opp alerts hvis pass rate dropper under threshold
### MLflow 3 (Databricks / Cross-platform)
MLflow 3 tilbyr komprehensiv GenAI-evaluering for agenter paa tvers av plattformer:
| Feature | Beskrivelse |
|---------|-------------|
| **Built-in LLM judges** | Innebygde dommere for kvalitetsmetrikker (relevance, groundedness, safety, etc.) |
| **Custom scorers** | Definer egne kvalitetsmetrikker med Python-funksjoner |
| **Eval harness** | Test GenAI-app mot eval-datasett under utvikling; sammenlign appversjoner |
| **Conversation evaluation** | Vurder multi-turn samtalekvaltiet (completeness, user frustration, dialogue coherence) |
| **Conversation simulation** | Generer syntetiske multi-turn samtaler for testing |
| **Production monitoring** | Kjoer scorers og judges paa produksjons-traces automatisk (Beta) |
| **Review App** | Samle ekspertfeedback og bygg eval-datasett |
MLflow Tracing gir real-time trace logging gjennom hele livssyklusen. Samme judges og scorers kan brukes i baade development og produksjon — konsistent evaluering.
```python
# MLflow 3 evaluation eksempel
import mlflow
results = mlflow.genai.evaluate(
data=eval_dataset,
predict_fn=my_agent,
scorers=[mlflow.genai.scorers.groundedness(), mlflow.genai.scorers.safety()]
)
```
### Prompt Flow
- **Evaluation flows:** Custom evaluation logic som Prompt Flow (deprecated approach — bruk Azure AI Evaluation SDK i stedet)
@ -488,7 +515,7 @@ evaluator = TaskAdherenceEvaluator(
9. **Evaluate and monitor AI agents (MLflow 3 on Databricks)**
https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/eval-monitor/
*Confidence: Verified* — MLflow-based evaluation for cross-platform agents
*Confidence: Verified* — MLflow 3 GenAI evaluation: built-in LLM judges og scorers, eval-harness for development, production monitoring (Beta), conversation evaluation (multi-turn), conversation simulation, Review App for human feedback, Genie Code for observability; integrert med MLflow Tracing paa tvers av development/test/produksjon; oppdatert 2026-04
10. **Run automated tests for agent quality and reliability (Copilot Studio)**
https://learn.microsoft.com/en-us/power-platform/release-plan/2025wave1/microsoft-copilot-studio/run-automated-tests-agent-quality-reliability

11
plugins/ms-ai-architect/skills/ms-ai-engineering/references/agent-orchestration/agent-memory-and-context-management.md
View file

@ -1,6 +1,6 @@
# Agent Memory and Context Management Strategies
**Last updated:** 2026-02
**Last updated:** 2026-04 | Verified: MCP 2026-04
**Status:** GA (Managed Memory in Foundry Agent Service: Preview)
**Category:** Agent Orchestration & Automation
@ -189,9 +189,12 @@ var agent = new ChatClientAgent(
```
**Azure Copilot BYOS (Bring Your Own Storage):**
- Organisations-kontrollert Cosmos DB instance
- Audit trail av alle samtaler
- Managed identity-basert tilgang
- Organisasjonen velger og administrerer sin egen Azure Cosmos DB-instans
- Full audit trail av alle Azure Copilot-samtaler (user prompts + Copilot responses) for alle tenant-brukere
- System-assigned managed identity med `Cosmos DB Built-in Data Contributor`-rollen for sikker lese-/skrivetilgang
- Aktiveres via Azure Copilot admin center → Conversation storage
- **OBS:** Hvis BYOS aktiveres, mister brukere tilgang til samtaler lagret av Microsoft foer aktivering. Bytte av Cosmos DB-instans gir tilsvarende tap av tilgang til tidligere instans.
- **OBS:** BYOS deaktiverer for oeyeblikket migration agent-kapabiliteter i Azure Copilot
**Fordeler:**
- Full data control og compliance

30
plugins/ms-ai-architect/skills/ms-ai-engineering/references/agent-orchestration/agent-to-agent-a2a-protocol.md
View file

@ -1,6 +1,6 @@
# Agent2Agent (A2A) Protocol — Åpen Standard for Agent-Interoperabilitet
**Last updated:** 2026-02
**Last updated:** 2026-04 | Verified: MCP 2026-04
**Status:** Preview (Microsoft-implementasjoner) / GA (protokollspesifikasjon v0.3)
**Category:** Agent Orchestration & Automation
@ -210,6 +210,8 @@ A2A og MCP (Model Context Protocol) løser forskjellige problemer og er kompleme
| **Transparens** | Intern logikk er ugjennomsiktig for kallende agent | Orkestrator ser og kontrollerer all verktøybruk |
| **Beste for** | Agenter eid av forskjellige team/org, kompleks delegering | Enkelt, kontrollert tilgang til APIer og data |
**A2A-melding inkluderer rik metadata:** Hver A2A-melding inneholder et unikt `contextId`, `messageId`, locale-info, full chat-historikk (ikke bare siste melding), og content parts (tekst, tool calls, etc.). Downstream agenter kan bruke denne metadata til routing, kontekst og kontinuitet.
**Typisk kombinert bruk:**
```
@ -263,12 +265,24 @@ with AIProjectClient(endpoint=endpoint, credential=DefaultAzureCredential()) as
Copilot Studio kan konsumere A2A-agenter direkte:
1. Gå til **Agents** → **Add an agent** → **Connect to an external agent** → velg **Agent2Agent**
2. Angi endepunkt-URL (ikke URL for agent card, men kommunikasjonsendepunktet)
3. Copilot Studio henter automatisk navn og beskrivelse fra `/.well-known/agent.json`
4. Velg autentiseringsmetode
1. Gå til **Agents**-siden → **Add an agent** → **Connect to an external agent** → velg **Agent2Agent**
2. Angi endepunkt-URL (kommunikasjonsendepunktet, IKKE URL for agent card)
3. Copilot Studio henter automatisk navn og beskrivelse fra `/.well-known/agent.json` (standard well-known-URL). Hvis automatisk populering feiler, angi navn og beskrivelse manuelt
4. Velg autentiseringsmetode: **None**, **API key**, eller **OAuth 2.0**
5. Velg eller opprett connection, deretter **Add and configure**
**Viktig:** Copilot Studio er ansvarlig for å vurdere datadeling, sikkerhet og compliance for tilkoblede eksterne agenter.
**A2A vs HTTP connector — valg av integrasjonstype:**
| Behov | Anbefalt |
|-------|----------|
| Agenter som er bygget på eksterne rammeverk | A2A |
| Agenter hostet utenfor Copilot Studio | A2A |
| Multi-turn interaksjoner med domenespesifikk resonnering | A2A |
| Enkle API-kall eller HTTP-tjenester | Custom connectors |
| MCP-verktøy og ressurser | MCP-servere |
| Microsoft 365 Agents SDK-agenter | Activity Protocol |
**Viktig:** Tilkobling til A2A-agenter utenfor Copilot Studio gir brukeransvar for datadeling, sikkerhet, compliance og kvalitetssikring.
### Semantic Kernel
@ -614,11 +628,11 @@ app.MapA2A(agent, "/a2a/my-agent", agentCard: new()
3. **Copilot Studio — Connect A2A Agent**
- https://learn.microsoft.com/microsoft-copilot-studio/add-agent-agent-to-agent
- Confidence: **Verified** (offisiell guide, februar 2026)
- Confidence: **Verified** (offisiell guide, oppdatert 2026-04: oppsettstrinn, autentiseringsalternativer, A2A vs HTTP connector-tabell)
4. **Multi-agent Patterns — MCP vs A2A**
- https://learn.microsoft.com/microsoft-copilot-studio/guidance/architecture/multi-agent-patterns
- Confidence: **Verified** (Copilot Studio arkitekturguide, februar 2026)
- Confidence: **Verified** (Copilot Studio arkitekturguide, oppdatert 2026-04: MCP vs A2A capability-matrise, hybrid workflow-anbefalinger, Agent 365 control plane)
5. **Azure API Management — A2A Agent API**
- https://learn.microsoft.com/azure/api-management/agent-to-agent-api

14
plugins/ms-ai-architect/skills/ms-ai-engineering/references/agent-orchestration/agent-to-agent-communication.md
View file

@ -1,6 +1,6 @@
# Agent-to-Agent Communication Protocols
**Last updated:** 2026-02
**Last updated:** 2026-04 | Verified: MCP 2026-04
**Status:** GA
**Category:** Agent Orchestration & Automation
@ -237,8 +237,14 @@ eventBus.Subscribe<AgentCompletedEvent>(async evt =>
| Topologi | Beskrivelse | Use Case |
|----------|-------------|----------|
| **Broker Topology** | Agenter broadcaster events, andre agenter reagerer eller ignorerer | Dynamiske workflows, ingen sentral koordinering |
| **Mediator Topology** | En mediator styrer event flow og state | Komplekse workflows med error handling og state management |
| **Broker Topology** | Agenter broadcaster events, andre agenter reagerer eller ignorerer. Hoey dekobling, men mangler innebygd error handling og distributed transaction-stoette | Dynamiske workflows, ingen sentral koordinering |
| **Mediator Topology** | En mediator styrer event flow og state, dispatcher commands til dedikerte kanaler. Bedre feilhaandtering og datakonsistens, men oekt kobling og potensielt bottleneck | Komplekse workflows med error handling og state management |
**Event-driven utfordringer ved agent-kommunikasjon:**
- **Eventual consistency:** Data paa tvers av agenter er ikke umiddelbart konsistent. Design for dette bevisst.
- **Ordering:** Ved skalering kan events motttas i feil rekkefoelge. Bruk partisjonsnoekler og idempotent processing.
- **Observability:** Inkluder correlation ID i alle events fra start — retrofit er vanskelig.
- **Schema evolution:** Definer versjonsstrategi tidlig. Design consumers til aa haandtere ukjente event-versjoner.
**Når brukes dette:**
- Lang-levende workflows (timer/dager)
@ -538,7 +544,7 @@ Trenger dere agent discovery?
4. **Event-Driven Architecture (Azure)**
- https://learn.microsoft.com/en-us/azure/architecture/guide/architecture-styles/event-driven
- Confidence: **Verified** (Azure Architecture Center, 2024)
- Confidence: **Verified** (Azure Architecture Center, oppdatert 2026-04: broker vs mediator topology, eventual consistency, ordering, observability, schema evolution)
5. **Azure Service Bus Integration**
- https://learn.microsoft.com/en-us/dotnet/architecture/microservices/multi-container-microservice-net-applications/integration-event-based-microservice-communications

22
plugins/ms-ai-architect/skills/ms-ai-engineering/references/agent-orchestration/computer-using-agents-cua.md
View file

@ -1,6 +1,6 @@
# Computer-Using Agents (CUA)
**Last updated:** 2026-02
**Last updated:** 2026-04 | Verified: MCP 2026-04
**Status:** Preview (sep 2025 — Foundry Agent Service; mai 2025 — Copilot Studio)
**Category:** Agent Orchestration & Automation
@ -109,12 +109,15 @@ Copilot Studio tilbyr CUA som et lavkode **Computer Use Tool** — ingen koding
### Oppsett
1. Gå til **Tools** i agenten → **Add tool** → **Computer use**
2. Velg modell: OpenAI Computer-Using Agent eller Anthropic Claude Sonnet 4.5
3. Skriv instruksjoner på naturlig norsk/engelsk
1. Gå til **Tools** i agenten → **Add tool** → **New tool** → **Computer use**
2. Velg modell: **OpenAI Computer-Using Agent** eller **Anthropic Claude Sonnet 4.5** (preview, pågående regionutrulling — krever at administrator har aktivert external models)
3. Skriv instruksjoner på naturlig norsk/engelsk (se "Best practices for instructions" under)
4. Konfigurer **Machine** (hvor CUA kjøres):
- **Hosted browser** (Windows 365 for Agents) — rask start, kun web, ikke anbefalt for produksjon
- **Dedikert Windows-maskin** — gir full desktop-tilgang, anbefalt for produksjon
- Velg målmaskin fra listen, eller opprett ny via Power Automate Portal
- **Hosted browser**: rask start, kun web — ikke anbefalt for produksjon
- **Dedikert Windows-maskin**: gir full desktop-tilgang, anbefalt for produksjon
**Merk:** Tilgangskontroll (access control) hindrer kun at modellen *utfører handlinger* på ikke-autoriserte nettsider/apper — ikke at de åpnes. Eksempel: Bing kan åpnes fra Edge-søkebaren selv om kun microsoft.com er på allowlisten, men interaksjon med Bing vil feile.
### Credentials og tilgangskontroll
@ -122,12 +125,13 @@ 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 |
### Lisensiering (Copilot Studio, preview)
Faktureres som Agent action: **5 Copilot Credits per steg**.
Faktureres som Agent action: **5 Copilot Credits per steg** (hvert steg kan inneholde én eller flere lavnivå-handlinger som klikk, skriving eller navigering).
Eksempel — utfylling av timeregistreringsskjema (4 steg = 20 Copilot Credits):
1. Åpne nettleser og naviger til URL
@ -481,7 +485,7 @@ 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, 2025)
- Confidence: **Verified** (offisiell Copilot Studio preview-dokumentasjon, oppdatert 2026-04: støttede modeller, credentials, access control-semantikk)
3. **Configure where computer use runs**
- https://learn.microsoft.com/microsoft-copilot-studio/configure-where-computer-use-runs
@ -497,7 +501,7 @@ Kostnader basert på:
6. **FAQ for the computer use tool**
- https://learn.microsoft.com/microsoft-copilot-studio/faqs-computer-use
- Confidence: **Verified** (offisiell FAQ, inkl. 80%/35% suksessrater)
- Confidence: **Verified** (offisiell FAQ, inkl. 80%/35% suksessrater, human supervision-detaljer, oppdatert 2026-04)
7. **Computer Use Release Plan (2025 Wave 1)**
- https://learn.microsoft.com/power-platform/release-plan/2025wave1/microsoft-copilot-studio/automate-web-desktop-apps-computer-use

6
plugins/ms-ai-architect/skills/ms-ai-engineering/references/agent-orchestration/foundry-workflows-visual-orchestration.md
View file

@ -1,6 +1,6 @@
# Foundry Workflows — Visuell Multi-Agent Orkestrering
**Last updated:** 2026-02
**Last updated:** 2026-04 | Verified: MCP 2026-04
**Status:** Public Preview (announced MS Ignite november 2025)
**Category:** Agent Orchestration & Automation
@ -133,6 +133,8 @@ Human-in-the-loop er et førsteklasses konsept i Foundry Workflows. Workflowen *
falsePath: return_for_revision
```
**Agent Framework HITL (pro-code):** Bruk `RequestPort` (C#) eller `ctx.request_info()` (Python) for HITL i egendefinerte workflows. For agent orchestrations (sequential, concurrent, group chat): bruk `ToolApprovalRequestContent` — agenten kan markere tools som approval-required, workflow pauser og emitter `RequestInfoEvent`. Checkpoints bevarer pending requests ved gjenopptak.
### Detaljer: Loop-noden (For each)
```yaml
@ -598,7 +600,7 @@ Foundry Workflows' visuelle designer gir offentlig sektor-organisasjoner en unik
5. **Declarative Workflows — Overview (Agent Framework)**
- https://learn.microsoft.com/agent-framework/workflows/declarative
- Confidence: **Verified** (YAML patterns, looping, HITL, error handling)
- Confidence: **Verified** (YAML action types tabell: Variable Management, Control Flow, Agent/Tool Invocation, HITL, Conversation — C# og Python; oppdatert 2026-04)
6. **Human-in-the-Loop Workflows**
- https://learn.microsoft.com/agent-framework/workflows/human-in-the-loop

29
plugins/ms-ai-architect/skills/ms-ai-engineering/references/agent-orchestration/multi-agent-orchestration-patterns.md
View file

@ -1,6 +1,6 @@
# Multi-Agent Orchestration Patterns and Topologies
**Last updated:** 2026-02
**Last updated:** 2026-04 | Verified: MCP 2026-04
**Status:** GA
**Category:** Agent Orchestration & Automation
@ -429,11 +429,24 @@ await Task.WhenAll(taskA, taskB); // Checkpoint ensures no replay
- Anbefales for inter-platform orchestration
**Multi-Agent Pattern Recommendations (Microsoft Copilot Studio):**
1. Prefer platform-native orchestration for internal flows
2. Use MCP for tool/data access (M365 services)
3. Use A2A for cross-platform messaging
4. Integrate mature agents via MCP or A2A
5. Enforce policy/auditing via Agent 365 control plane
1. Prefer platform-native orchestration for internal flows with subagents
2. Use MCP for tool and data access (M365 services) — enterprise-grade security, authentication, auditing
3. Use A2A for cross-platform agent-to-agent messaging — design for capability discovery and task contracts
4. Integrate mature or abstracted agents via MCP or A2A for reuse and end-to-end traceability
5. Enforce policy and auditing at control-plane layer with Agent 365
6. Use least-privileged scope when calling MCP-hosted tools
7. Design for parallelism, limit inter-agent context to strictly necessary, use short-term memory
8. Include users in workflow — require human approvals for high-impact cross-agent actions
**MCP vs A2A — nar bruke hva (oppdatert fra Copilot Studio multi-agent-patterns):**
| Kapabilitet | MCP | A2A |
|-------------|-----|-----|
| Multimodalitet | Krever at MCP host stoetter det | Annonserer stoettede medietyper |
| Multi-turn interaksjoner | Valgfri elicitation. Kontekst hos host | contextId haandterer kontekst paa tvers av agenter |
| Orkestrering | Host orkestrerer hvilke tools som kalles | Invokert agent bruker sin egen chain-of-thought |
| Forhandling | Krever klientoppdatering | Dynamisk forhandling, robust mot serviceoppdateringer |
| Beste for | Full kontroll over resonnering, kontrollerte scenarios | Opake agenter, cross-org, ekstern agent |
### Semantic Kernel + Agent Framework
@ -638,7 +651,7 @@ scope.RecordOutputMessages(new[] { output });
1. **AI agent orchestration patterns** (Azure Architecture Center)
https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/ai-agent-design-patterns
*Confidence: Verified* — Definitive guide til alle 5 patterns
*Confidence: Verified* — Definitive guide til alle 5 patterns, spektrum av kompleksitet (direct model call → single agent → multi-agent), oppdatert 2026-04
2. **Microsoft Agent Framework Workflows Orchestrations**
https://learn.microsoft.com/en-us/agent-framework/user-guide/workflows/orchestrations/overview
@ -658,7 +671,7 @@ scope.RecordOutputMessages(new[] { output });
6. **Multi-agent patterns (Copilot Studio)**
https://learn.microsoft.com/en-us/microsoft-copilot-studio/guidance/architecture/multi-agent-patterns
*Confidence: Verified* — A2A protocol, MCP integration
*Confidence: Verified* — A2A protocol, MCP integration, capability matrise, hybrid workflow diagram, oppdatert 2026-04
7. **Build agent platforms on Azure** (Microsoft for Startups)
https://learn.microsoft.com/en-us/microsoft-for-startups/build/build-agent

10
plugins/ms-ai-architect/skills/ms-ai-engineering/references/agent-orchestration/semantic-kernel-agents-implementation.md
View file

@ -1,6 +1,6 @@
# Semantic Kernel and Microsoft Agent Framework - Implementation Patterns
**Last updated:** 2026-02
**Last updated:** 2026-04 | Verified: MCP 2026-04
**Status:** GA (Agent Orchestration: Experimental)
**Category:** Agent Orchestration & Automation
@ -235,6 +235,12 @@ Trenger du OpenAI Assistants API features (code interpreter, retrieval)?
### Velg Orchestration Pattern
**Foer du velger multi-agent pattern:** Evaluer om scenariet faktisk krever det. Hvert kompleksitetsnivaa introduserer koordinerings-overhead, latens og kostnad. Bruk laveste kompleksitetsnivaa som tilfredsstillende loser problemet:
1. **Direct model call** — klassifisering, oppsummering, oversettelse (ingen agent)
2. **Single agent med tools** — varierte spoerringsmaal innen ett domene (ofte riktig default)
3. **Multi-agent orchestration** — cross-domain, ulike sikkerhetsbegrensninger, eller oppgaver som drar nytte av parallell spesialisering
| Scenario | Anbefalt Pattern | Hvorfor |
|----------|------------------|---------|
| Uavhengige subtasks | Concurrent | Parallell utførelse, redusert total tid |
@ -459,7 +465,7 @@ Kernel agentKernel = sharedKernel.Clone(); // Rebruk AI service connections
5. [Microsoft Agent Framework Overview](https://learn.microsoft.com/en-us/agent-framework/overview/agent-framework-overview) — Confidence: Verified (2026-02)
6. [Magentic Orchestration Pattern](https://learn.microsoft.com/en-us/agent-framework/user-guide/workflows/orchestrations/magentic) — Confidence: Verified (2026-02)
7. [Microsoft 365 Agents SDK - Semantic Kernel Integration](https://learn.microsoft.com/en-us/microsoft-365/agents-sdk/using-semantic-kernel-agent-framework) — Confidence: Verified (2026-02)
8. [AI Agent Orchestration Patterns (Azure Architecture)](https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/ai-agent-design-patterns) — Confidence: Verified (2026-02)
8. [AI Agent Orchestration Patterns (Azure Architecture)](https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/ai-agent-design-patterns) — Confidence: Verified (oppdatert 2026-04: start med riktig kompleksitetsnivaa — direct model call, single agent med tools, multi-agent; guidance om naar multi-agent er hensiktsmessig)
### Kodeeksempler (Verified via MCP Code Search)

40
plugins/ms-ai-architect/skills/ms-ai-engineering/references/agent-orchestration/tool-use-and-function-calling-patterns.md
View file

@ -1,6 +1,6 @@
# Tool Use and Function Calling - Advanced Patterns
**Last updated:** 2026-02
**Last updated:** 2026-04 | Verified: MCP 2026-04
**Status:** GA
**Category:** Agent Orchestration & Automation
@ -207,19 +207,35 @@ var result = await mainAgent.RunAsync("Hvordan er været i Oslo?");
**Implementering (AG-UI + Agent Framework):**
```python
# AG-UI middleware for approval
def approval_middleware(tool_call):
if tool_call.name in ["delete_record", "send_email"]:
user_response = input(f"Approve {tool_call.name}? (yes/no): ")
return user_response.lower() == "yes"
return True # Auto-approve andre funksjoner
AG-UI backend tool rendering stoetter HITL via to mekanismer:
# Agent med approval-workflow
agent = ChatAgent(chat_client=client, tools=[delete_record, send_email])
# Koble approval_middleware til agent runtime
**C# - ApprovalRequiredAIFunction:**
```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));
}
}
```
**Python - @tool med approval_mode:**
```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)
# Workflow pauser og emitter function_approval_request event
# Klient-loop maa haandtere og respondere
```
**Backend tool events streames til klient i sanntid:** TOOL_CALL_START, TOOL_CALL_ARGS, TOOL_CALL_END, TOOL_CALL_RESULT.
---
## Beslutningsveiledning
@ -412,7 +428,7 @@ 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 2026-02**
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)
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**