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[Beginner] Multi Agent Implementation with Microsoft Agent Framework using Microsoft Azure

Hands On Labs to move from single AI agent to a coordinated multi-agent team using Microsoft Agent Framework and Microsoft Azure

By Manish Kumar - Enterprise AI Architect

What you'll learn

  • Build single AI agents with sessions, memory, instructions, and tools.
  • Connect agents securely with Azure OpenAI and Azure Key Vault.
  • Design multi-agent workflows with specialist roles and controlled handoffs.
  • Integrate external tools using Model Context Protocol (MCP).
  • Implement research, enterprise claims, and voice-enabled agent scenarios.

Skills you'll gain

Agent DevelopmentSession MemoryTool IntegrationMulti-Agent OrchestrationVoice Integration

Tools you'll use

PythonMicrosoft Agent FrameworkGitHubMicrosoft Azure OpenAI

Course content 5 modules · 5 topics · 1h 0m total

▸Module 1: Agent Framework FoundationsBuild AI agents with instructions, sessions, memory, and contextual conversations.1 topics · 12m
🔒Agent Framework Foundations12m
▸Module 2: From One Agent to an Agent TeamExplain memory, agent handoffs, workflows, MCP, research harnesses, and the planned specialist-agent scenario.1 topics · 12m
🔒From One Agent to an Agent Team12m
▸Module 3: Build and Run a Single AgentWalk through the Python code, client, instructions, session, and running the first agent.1 topics · 12m
🔒Build and Run a Single Agent12m
▸Module 4: Set Up the Azure LabGuide participants through the project files, Azure OpenAI resource, model deployment, and Key Vault setup.1 topics · 12m
🔒Set Up the Azure Lab12m
▸Module 5: Troubleshooting, Tools and MemoryResolve setup issues, run the agent, then explain how tools and session memory are added.1 topics · 12m
🔒Troubleshooting, Tools and Memory12m

About this course

The hands-on session explored how to move from a single AI agent to a coordinated multi-agent team using Microsoft Agent Framework and Microsoft Azure. Across two practical labs, participants worked with sessions, memory, tools, workflows, MCP integration, research agents, and controlled agent handoffs. We also demonstrated a multi-agent motor-claims scenario with specialist agents and human-review boundaries, followed by a voice-enabled AI interface. The session concluded with an engaging Q&A on agent orchestration, security, loop control, and when multi-agent architecture is truly required.

Requirements

  • Basic Python Knowledge
  • Basic VS Code
  • Basic GitHub
  • Basic Microsoft Azure

Who this course is for

  • Students
  • Working Professionals
  • Technology Leaders
  • CTOs

Your trainer

Manish Kumar - Enterprise AI Architect
Trainer
Manish Kumar - Enterprise AI Architect
Enterprise AI Architect
Trainer
Manish Kumar - Enterprise AI Architect
Enterprise AI Architect

Enterprise AI Architect with 13+ Years of experience in Data Analytics , BI & AI/ML

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