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docs(architect): weekly KB update — 66 files refreshed (2026-04)

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Updated 66 stale knowledge base reference files (10 critical, 56 high)
across all 5 skills using Microsoft Learn MCP research.

Key factual updates:
- Groundedness Detection API: `correction` → `mitigating` param,
  `correctedText` → `correctionText` (breaking change)
- Copilot Studio: GPT-4.1 mini now default (was GPT-4o mini);
  Claude Sonnet 4.5 + Opus 4.5 added (experimental, 200K ctx)
- Agentic Retrieval: still public preview; 50M free tokens/month
- Azure security baselines: "Cognitive Services" → "Foundry Tools"
- Databricks: Delta Live Tables → Lakeflow Spark Declarative Pipelines
- MLflow 3 GenAI: new Feedback/Expectation data model
- Token tracking doc: "Azure OpenAI in Foundry Models through a gateway"
- Agent Registry: Risks column (M365 E7), Graph API (preview)
- Copilot DLP: new Entra AI Admin + Purview Data Security AI Admin roles
- ISO/IEC 42001: scope expanded to M365 Copilot, Foundry, Security Copilot
- Zero Trust: CAE now via Conditional Access, Strict Location Enforcement
- Purview: new Fabric Copilots/agents governance section
- AG-UI HITL: ApprovalRequiredAIFunction (C#), @tool approval_mode (Python)

All files: Last updated → 2026-04, *(Verified MCP 2026-04)* markers added.
Build registry: 1341 URLs from 387 files (+2 new URLs).

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Kjell Tore Guttormsen 2026-04-09 22:41:26 +02:00
commit 565043dbde
73 changed files with 727 additions and 301 deletions
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7
skills/ms-ai-governance/references/responsible-ai/continuous-improvement-feedback-loops.md
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@ -1,6 +1,6 @@
# Continuous Improvement and Feedback Loops - Iterative Governance
**Last updated:** 2026-02
**Last updated:** 2026-04
**Status:** GA
**Category:** Responsible AI & Governance
@ -29,7 +29,10 @@ Microsoft implementerer feedback loops gjennom hele AI-livssyklusen – fra utvi
### 1. Production Data Collection
**Tracing og logging:**
- **MLflow Traces**: Fanger detaljerte execution traces med inputs, outputs og alle mellomsteg for hver interaksjon
- **MLflow Traces** / **MLflow 3 GenAI**: Fanger detaljerte execution traces med inputs, outputs og alle mellomsteg for hver interaksjon. *(Verified MCP 2026-04)*
- MLflow 3 GenAI introduserer ny **Feedback/Expectation-datamodell** for strukturert lagring av human feedback
- `mlflow.log_feedback()` API for å knytte bruker-rating og kommentarer til spesifikke traces
- Integrert tracing for Databricks agentic applikasjoner
- **Azure Monitor & Application Insights**: Logger operational metrics, latency, error rates
- **Model Data Collector**: Automatisk innsamling av production data for ML-modeller
- **Azure AI Content Safety logs**: Sporer content moderation events