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Microsoft AI-500 : Designing and Implementing Multi-Agent AI Solutions

AI-500

Exam Code: AI-500

Exam Name: Designing and Implementing Multi-Agent AI Solutions

Updated: Aug 18, 2026

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Microsoft AI-500 Exam Syllabus Topics:

SectionWeightObjectives
Secure, govern, and deploy multi-agent solutions20–25%- Apply security and compliance
  • 1. Enforce data protection and privacy
    • 2. Configure authentication and access control
      • 3. Manage governance and audit requirements
        - Deploy and maintain solutions
        • 1. Manage lifecycle and retirement
          • 2. Implement versioning and update strategies
            • 3. Deploy agents to production environments
              Develop multi-agent solutions in Azure30–35%- Implement agents using Azure AI services
              • 1. Build agents with Azure AI Agent Service
                • 2. Orchestrate workflows with Azure AI Foundry
                  • 3. Integrate tools, plugins, and APIs
                    - Manage state and memory
                    • 1. Handle multi-turn conversations
                      • 2. Use frameworks like Microsoft Agent Framework and MCP
                        • 3. Configure short-term and long-term memory
                          Architect multi-agent solutions15–20%- Design workflow and tool integration
                          • 1. Incorporate human-in-the-loop oversight
                            • 2. Apply responsible AI principles
                              • 3. Plan tool ecosystems and permissions
                                - Design logical architecture for multi-agent systems
                                • 1. Define agent patterns and roles
                                  • 2. Specify autonomy levels and guardrails
                                    • 3. Design agent communication and handoff protocols
                                      Evaluate, optimize, and monitor multi-agent solutions20–25%- Assess performance and reliability
                                      • 1. Optimize latency and scalability
                                        • 2. Diagnose failures and bottlenecks
                                          • 3. Define and measure success metrics
                                            - Implement observability
                                            • 1. Enable logging and tracing
                                              • 2. Use Azure-native monitoring tools
                                                • 3. Monitor agent interactions and outcomes

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