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Demo: Zava AI Shopping Assistant
Multi-Agent Architecture with A2A Protocol - Overview

Costa Rica

GitHub brown9804

Last updated: 2025-12-03

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!

image

Important

The deployment process typically takes 15-20 minutes

  1. Adjust terraform.tfvars values
  2. Initialize terraform with terraform init. Click here to understand more about the deployment process
  3. Run terraform apply, you can also leverage terraform apply -auto-approve.

Key Features

  • A2A Protocol Implementation: Complete Agent-to-Agent communication framework with standardized messaging, event handling, and task coordination
  • Multi-Agent Architecture: Specialized AI agents working through A2A protocol:
    • Cora (Shopper): Front-facing assistant for general queries
    • Inventory Manager: Checks stock availability via A2A requests
    • Customer Loyalty: Manages rewards and discounts through agent coordination
    • Cart Manager: Handles shopping cart operations with inter-agent communication
  • 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 including A2A framework deployment
  • A2A Intelligent Routing: Enhanced Handoff Service that supports both traditional routing and A2A protocol agent discovery
  • Data Pipeline Automation: Automatically ingests product catalogs with A2A event notifications and coordination

About A2A Protocol

A2A (Agent-to-Agent) Protocol is a standardized communication framework that enables multiple AI agents to collaborate and coordinate tasks seamlessly.

What is A2A Protocol?

  • Agent-to-Agent Communication: Structured messaging between multiple AI agents
  • Task Coordination: Agents can delegate tasks to specialized agents
  • Event-Driven Architecture: Real-time event handling for agent interactions
  • Agent Discovery: Automatic detection and registration of available agents
  • Protocol Standardization: Consistent API for inter-agent communication

A2A Components in This Project:

  • Agent Execution Framework: Manages multiple agent instances (src/a2a/server/agent_execution.py)
  • Event Queue System: Handles inter-agent communication (src/a2a/server/events/)
  • Task Management: Coordinates work between agents (src/a2a/server/tasks.py)
  • Request Handlers: Processes agent-to-agent requests (src/a2a/server/request_handlers.py)
  • Coordinator Agent: Orchestrates multi-agent workflows (src/a2a/agent/coordinator.py)
  • API Endpoints: RESTful and WebSocket APIs for agent communication (src/a2a/api/)

A2A vs Traditional Multi-Agent Systems:

  • Standardized Protocol: Uses consistent message formats and APIs
  • Scalable Architecture: Easily add new agents without modifying existing ones
  • Real-time Communication: WebSocket support for instant agent interactions
  • Event-Driven: Asynchronous event handling for better performance
  • Infrastructure Integration: Full Terraform deployment with monitoring and automation

Architecture

graph TD
    User[User] <--> UI[Chat Interface]
    UI <--> App[FastAPI Application]
    App <--> A2A[A2A Protocol Server]
    A2A <--> EventQueue[Event Queue]
    A2A <--> Coordinator[A2A Coordinator]
    
    Coordinator -->|A2A Protocol| Router{Agent Router}
    Router -->|Task Delegation| Cora[Cora Agent]
    Router -->|Design Tasks| Design[Interior Design Agent]
    Router -->|Inventory Events| Inventory[Inventory Agent]
    Router -->|Loyalty Tasks| Loyalty[Loyalty Agent]
    Router -->|Cart Events| Cart[Cart Agent]
    
    subgraph "A2A Communication"
        EventQueue <--> Cora
        EventQueue <--> Design
        EventQueue <--> Inventory
        EventQueue <--> Loyalty
        EventQueue <--> Cart
    end
    
    Inventory -->|Query| Search[Azure AI Search]
    Inventory -->|Lookup| Cosmos[Cosmos DB]
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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).

    • Sets up A2A protocol infrastructure including event queues and monitoring.

      image

  2. A2A Framework Deployment:

    • Initializes the Agent-to-Agent protocol server components.
    • Sets up event queue system for inter-agent communication.
    • Configures agent discovery and registration services.
    • Deploys A2A monitoring and automation frameworks.

  3. Data Pipeline Execution:

    • Sets up a Python virtual environment.

    • Ingests product_catalog.csv into Cosmos DB with A2A event notifications.

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

    • Creates and populates an Azure AI Search index with vector embeddings through A2A coordination.

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

  4. Agent Creation & A2A Registration:

    • Installs the azure-ai-projects SDK.

    • Connects to Azure AI Foundry.

    • Provisions 5 real agents with A2A protocol integration and specific instructions.

    • Registers agents with the A2A discovery service.

    • Saves the unique Agent IDs and A2A endpoints to the .env file.

      image

  5. Application Deployment:

    • Builds the Docker container with A2A protocol support in the cloud (ACR Build).
    • Configures the Azure Web App with the generated Agent IDs, A2A endpoints, and credentials.
    • Deploys the container with A2A server components 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 with A2A protocol support.

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

  2. Verify A2A Protocol Endpoints:

    • Check A2A Chat API: https://<your-app-name>.azurewebsites.net/a2a/chat
    • Check A2A Server API: https://<your-app-name>.azurewebsites.net/a2a/api/docs
    • Verify agent discovery: https://<your-app-name>.azurewebsites.net/a2a/server/agents

  3. Verify Agents:

    • Go to the Azure AI Foundry Portal.

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

    • You should see all 5 agents listed with A2A protocol integration.

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

  4. Test A2A Interactions: For example:

    • General: "Hi, who are you?" (Handled by Cora via A2A protocol)
    • Inventory: "Do you have the classic leather sofa in stock?" (Routed through A2A to Inventory Agent)
    • Design: "What colors of green paint do you have?" (A2A task delegation to Design Agent)
    • Multi-Agent: "Find a sofa and check my loyalty points" (A2A coordination between multiple agents)
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Refresh Date: 2025-12-03