AI-500: Design and implement multi-agent AI solutions

This advanced course focuses on the practical skills required to architect, build, deploy, govern, and operate production-ready multi-agent AI solutions using Microsoft Foundry and Azure. Learners develop expertise in multi-agent architecture, orchestration, governance, monitoring, security, and operational management.

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.

Learners should have: 

• Completion of AI-103 "Develop AI agents on Azure" or equivalent hands-on experience.
• Experience with Microsoft Foundry Agent Service and Microsoft Agent Framework.  
• Familiarity with multi-agent orchestration patterns and agent communication concepts.  
• Experience with GitHub Actions, Azure deployment concepts, and Azure security services. 
• Knowledge of responsible AI principles and governance practices.  
• Experience with OpenTelemetry, Azure Monitor, and evaluation frameworks for AI systems.  
• Strong Python programming skills and experience using Azure AI SDKs.  
• Access to Microsoft Foundry and relevant Azure services for hands-on activities.

After completing this course, students will be able to:

• Design production-grade multi-agent AI architectures.
• Implement advanced orchestration and agent collaboration patterns.
• Build scalable agent communication solutions using A2A protocols.
• Create advanced prompting strategies for enterprise AI agents.
• Develop enterprise-grade tool ecosystems using MCP and Microsoft Foundry.
• Implement advanced RAG solutions using Azure AI Search.
• Design persistent memory architectures using Azure Cosmos DB.
• Deploy multi-agent solutions through automated CI/CD pipelines.
• Secure agent ecosystems using Azure zero-trust principles.
• Implement enterprise governance, monitoring, evaluation, and incident response processes for multi-agent AI systems.

1. Design stateful agentic loops with Microsoft Foundry agent service

Learn how to design stateful agent workflows for production environments, including session management, context handling, planning cycles, run-status management, and migration to modern agent APIs.

2. Implement advanced multi-agent orchestration patterns in Microsoft Foundry

Explore advanced orchestration approaches such as hub-and-spoke architectures, hierarchical supervision, concurrent execution, agent synchronisation, and recovery from failures.

3. Apply task decomposition and agent collaboration strategies in Microsoft Foundry

Learn techniques for breaking complex problems into manageable tasks, coordinating agent interactions, preserving context across handoffs, and balancing performance with coordination overhead.

4. Design enterprise-scale agent communication with A2A in Azure

Design communication architectures that enable large-scale agent collaboration using the A2A protocol, shared state management, routing, conflict resolution, and multitenant considerations.

5. Design advanced prompting strategies for production AI agents

Develop sophisticated prompting techniques for production AI agents, including multi-turn reasoning, prompt optimisation, version control, persona management, and prompt injection defence.

6. Build enterprise-grade tool ecosystems with MCP and Microsoft Foundry

Learn how to build and govern agent tool ecosystems, including custom MCP servers, authentication, tool routing, validation, versioning, and operational management.

7. Implement advanced RAG pipelines with Azure AI Search and Microsoft Foundry

Implement advanced retrieval-augmented generation solutions using Azure AI Search, hybrid search techniques, reranking, intelligent routing, and optimisation strategies.

8. Design multi-agent memory architectures with Azure Cosmos DB

Design long-term memory solutions for multi-agent systems, including vector storage, context retention, memory optimisation, and lifecycle management.

9. Implement CI/CD pipelines for multi-agent systems with GitHub Actions

Learn how to automate deployment and lifecycle management of multi-agent solutions using GitHub Actions, infrastructure as code, progressive delivery, and rollback strategies.

10. Secure multi-agent systems with Azure zero-trust architecture

Implement security best practices for multi-agent systems using managed identities, access controls, Key Vault integration, tenant isolation, encryption, and compliance controls.

11. Scale responsible AI governance with Azure AI Content Safety and Microsoft Foundry

Establish governance frameworks that address fairness, transparency, privacy, accountability, monitoring, and responsible AI compliance across agent ecosystems.

12. Govern the enterprise agent lifecycle in Microsoft Foundry

Manage the full lifecycle of enterprise agents through versioning, approval workflows, cost allocation, change management, behavioural risk assessment, and retirement planning.

13. Implement distributed observability for multi-agent solutions with OpenTelemetry

Build observability solutions that provide visibility into agent behaviour using distributed tracing, telemetry collection, structured logging, and anomaly detection.

14. Design evaluation frameworks for multi-agent solutions with Microsoft Foundry

Create evaluation methodologies that measure coordination quality, system outcomes, behavioural drift, and overall effectiveness of multi-agent systems.

15. Optimize multi-agent performance and cost in Microsoft Foundry

Learn how to improve efficiency through model routing, caching, token optimisation, context management, and analysis of cost-performance trade-offs.

16. Design human-in-the-loop approval workflows with Power Automate and Microsoft Teams

Design workflows that incorporate human review and approvals, enabling oversight, escalation, auditability, and continuous improvement of agent-driven processes.

17. Debug and respond to production multi-agent incidents in Azure

Develop structured approaches for diagnosing, investigating, reproducing, remediating, and preventing failures within complex multi-agent AI environments.