README.md

Demo: Zava AI Shopping Assistant
Multi-Agent Architecture - Overview

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brown9804

Last updated: 2025-11-12

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Important

Disclaimer: This repository contains a demo of Zava AI Shopping Assistant, a multi-agent system designed for e-commerce. It features a fully automated "Zero-Touch" deployment pipeline orchestrated by Terraform, which provisions infrastructure, ingests data, creates real AI agents in Azure AI Foundry, and deploys the application container. Please refer TechWorkshop L300: AI Apps and Agents, and if needed contact Microsoft directly: Microsoft Sales and Support more guindace. There are tons of free resources out there, all eager to support!

Key Features

• Multi-Agent Architecture: Few specialized AI agents working in concert:
  • Cora (Shopper): Front-facing assistant for general queries.
  • Inventory Manager: Checks stock availability.
  • Customer Loyalty: Manages rewards and discounts.
  • Cart Manager: Handles shopping cart operations.
• Real Azure AI Agents: Integrates with Azure AI Foundry to create and host persistent agents (not just local simulations).
• Zero-Touch Deployment: A single terraform apply command handles the entire lifecycle from infrastructure to application code.
• Intelligent Routing: A dedicated Handoff Service classifies user intent and routes messages to the appropriate specialist agent.
• Data Pipeline Automation: Automatically ingests product catalogs into Cosmos DB and builds Vector Search indexes.

Architecture

graph TD
    User[User] <--> UI[Chat Interface]
    UI <--> App[FastAPI Application]
    App <--> Handoff[Handoff Service]
    Handoff -->|Classifies Intent| Router{Router}
    
    Router -->|General| Cora[Cora Agent]
    Router -->|Design| Design[Interior Design Agent]
    Router -->|Stock| Inventory[Inventory Agent]
    Router -->|Rewards| Loyalty[Loyalty Agent]
    Router -->|Checkout| Cart[Cart Agent]
    
    Inventory -->|Query| Search[Azure AI Search]
    Inventory -->|Lookup| Cosmos[Cosmos DB]

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Important

The deployment process typically takes 15-20 minutes

What Happens Under the Hood?

When you run terraform apply, the following automated sequence occurs:

1. Infrastructure Provisioning:

   • Creates Resource Group, Cosmos DB, Azure AI Foundry, AI Search, Storage Account, Key Vault, and Container Registry (ACR).

   • Deploys AI Models (gpt-4o-mini, text-embedding-3-small).

     

2. Data Pipeline Execution:

   • Sets up a Python virtual environment.

   • Ingests product_catalog.csv into Cosmos DB.

     Ingests.product_catalog.csv.into.Cosmos.DB.mp4

   • Creates and populates an Azure AI Search index with vector embeddings.

     Creates.and.populates.an.Azure.AI.Search.index.with.vector.embeddings.mp4

3. Agent Creation:

   • Installs the azure-ai-projects SDK.

   • Connects to Azure AI Foundry.

   • Provisions 5 real agents with specific instructions and tool definitions

   • Saves the unique Agent IDs to the .env file.

     

4. Application Deployment:

   • Builds the Docker container in the cloud (ACR Build).
   • Configures the Azure Web App with the generated Agent IDs and credentials.
   • Deploys the container and restarts the app.

Verification

After deployment completes, verify the system:

1. Check the Web App:

   • The Terraform output will provide the application_url.

   • Visit https://<your-app-name>.azurewebsites.net.

   • You should see the Zava chat interface.

     Also.you.can.get.the.information.from.the.web.app.-.App.Service.mp4

2. Verify Agents:

   • Go to the Azure AI Foundry Portal.

   • Navigate to your project -> Build -> Agents.

   • You should see all 5 agents listed.

     Agents.and.LLMs.-.MSFT.Foundry.mp4

3. Test Interactions: For example:

   • General: "Hi, who are you?" (Handled by Cora)
   • Inventory: "Do you have the classic leather sofa in stock?" (Handled by Inventory Agent)
   • Design: "What colors of green paint do you have?"