Design and implement multi-agent AI solutions (AI-500)

Candidates for this course, are expert-level practitioners who have subject matter expertise in designing, building, and optimizing scalable, production‑ready, multi-agent AI systems, solutions and workflows. Out of your job role, you work closely with developers, machine learning engineers, platform engineers, data scientists, and business stakeholders to translate complex requirements into production-ready, multi-agent solutions.

After this course participants should be able to:

• Understand the architecture and core concepts of multi-agent AI solutions.
• Design scalable and production-ready AI systems using Microsoft Foundry and Azure.
• Build and integrate tools and services that enable effective agent collaboration.
• Implement multi-agent orchestration and workflow automation.
• Deploy enterprise-grade agentic AI solutions in Azure environments.
• Apply security, governance, and compliance best practices for AI implementations.
• Monitor, evaluate, and optimize the performance of AI agents and workflows.
• Manage end-to-end lifecycle operations for multi-agent AI solutions.
• Collaborate with technical and business stakeholders to deliver AI-driven outcomes.
• Develop the skills needed to architect, build, and operate advanced multi-agent AI solutions in production.

Module 1: Architect production-grade multi-agent AI solutions in Azure3.

• Design stateful agentic loops with Microsoft Foundry agent service
• Implement advanced multi-agent orchestration patterns in Microsoft Foundry
• Apply task decomposition and agent collaboration strategies in Microsoft Foundry
• Design enterprise-scale agent communication with A2A in Azure

Module 2: Build production-grade multi-agent capabilities with Microsoft Foundry

• Design advanced prompting strategies for production AI agents
• Build enterprise-grade tool ecosystems with MCP and Microsoft Foundry
• Implement advanced RAG pipelines with Azure AI Search and Microsoft Foundry
• Design multi-agent memory architectures with Azure Cosmos DB

Module 3: Deploy and govern enterprise agentic AI solutions on Azure

• Implement CI/CD pipelines for multi-agent systems with GitHub Actions
• Secure multi-agent systems with Azure zero-trust architecture
• Scale responsible AI governance with Azure AI Content Safety and Microsoft Foundry
• Govern the enterprise agent lifecycle in Microsoft Foundry

Module 4: Monitor, evaluate, and operate multi-agent AI solutions in Azure

• Implement distributed observability for multi-agent solutions with OpenTelemetry
• Design evaluation frameworks for multi-agent solutions with Microsoft Foundry
• Optimize multi-agent performance and cost in Microsoft Foundry
• Design human-in-the-loop approval workflows with Power Automate and Microsoft Teams
• Debug and respond to production multi-agent incidents in Azure

Participants should have a basic understanding of cloud computing concepts, artificial intelligence fundamentals, and software development principles. Prior experience with Azure services, programming, or AI solution development is beneficial but not mandatory.

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