Demo: Automating Recommendations from Azure Arc with an AI Agent (full-code approach)

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brown9804

Last updated: 2025-07-30

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Arc API → Function App → AI Foundry → Logic Apps → Monitoring

• Function App is the central orchestrator for ingestion and enrichment.
• AI Foundry provides decision intelligence.
• Logic Apps executes or escalates actions.
• Monitoring ensures observability and compliance.

Important

Disclaimer: This repository contains example of how to automate the recommendations from Azure Arc by introducing an AI-driven agent that not only ingests and processes recommendations but also:

• Classify & priority-rank each recommendation
• Summarize actionable next steps in natural language
• Decide auto-execute vs human-review paths This is just a guide. It is not an official solution. For official guidance, support, or more detailed information. Please refer RAG with Zero-Trust – Architecture Reference to Microsoft's official documentation or contact Microsoft directly: Microsoft Sales and Support

Category	Components	Purpose
Core Components	- Azure Arc API - Resource Group - Subscription	- Source of recommendations (DR, security, performance, compliance) for on-prem and hybrid assets. - Groups all resources under a single RG and subscription scope.
Data Engineering Pipeline	- Function App Every Function App requires a General-Purpose v2 Storage Account for triggers, state, and logging. - App Service Plan (Consumption/Premium SKU) The App Service Plan can be serverless (Consumption) or a dedicated tier (Premium/Dedicated). - Storage Account (General Purpose v2 for Functions runtime)	Hosts and scales your ingestion/enrichment logic; fetches recommendations and sends them to AI for processing.
AI Layer	AI Foundry	Classifies severity, summarizes actions, prioritizes recommendations, and suggests auto-execute vs manual review.
Automation & Orchestration	Logic Apps	Executes safe actions (DR failover, patching, SQL fixes) or sends Teams/Email approvals for high-risk items.
Monitoring & Governance	- Azure Monitor + Log Analytics Workspace - Power BI	Tracks pipeline health, AI decisions, execution outcomes; visualizes trends, compliance, and automation SLAs.

Overview

Workflow details (Click to expand)

1. Azure Arc API (Source)
   • Acts as the entry point for all recommendations (DR, security, performance, compliance).
   • Provides raw JSON data about advisories from on-prem and hybrid resources.
2. Function App (with App Service Plan + Storage Account): Ingest and process recommendations.
   • Periodically calls Azure Arc API to fetch recommendations.
   • Stores raw data temporarily in the Storage Account.
   • Sends the data to the AI layer for enrichment.
3. AI Foundry: Adds intelligence to the pipeline.
   • Receives raw recommendations from the Function App.
   • Uses LLM models to:
     • Classify severity (High/Medium/Low).
     • Summarize recommendations in plain language.
     • Suggest whether to auto-execute or require manual review.
   • Returns enriched recommendations back to the Function App for storage and orchestration.
4. Logic Apps: Orchestrates actions based on AI decisions.
   • Reads enriched recommendations.
   • If autoExecute = true, triggers automation tasks (e.g., DR failover, patching, SQL index creation).
   • If manualReview = true, sends Teams or email notifications for approval.
5. Monitoring & Governance:
   • Azure Monitor + Log Analytics Workspace:
     • Collects telemetry from Function App, Logic Apps, and AI calls.
     • Tracks pipeline health, execution outcomes, and AI decision logs.
   • Power BI: Connects to Log Analytics or SQL data to visualize.
     • Number of recommendations processed.
     • Auto-executed vs manual approvals.
     • SLA compliance and risk reduction trends.

Refresh Date: 2025-09-17