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

Design and Implement Multi-Agent AI Solutions AI-500

In this course you will develop the skills required to design, architect, build, deploy, monitor, secure, and govern production-ready multi-agent AI solutions using Microsoft Foundry and Azure.

Audience

Experienced practitioners responsible for:

• Designing scalable multi-agent AI systems
• Building and optimizing production-ready AI solutions
• Collaborating with developers, ML engineers, platform engineers, data scientists, and business stakeholders
• Translating business requirements into enterprise AI solutions

Design and Implement Multi-Agent AI Solutions Outline

Architect Production-Grade Multi-Agent AI Solutions in Azure

Designing Multi-Agent Solution Architecture

• Multi-agent design principles
• Agent interaction models
• System architecture patterns

Planning Enterprise AI Solutions

• Requirements analysis
• Scalability considerations
• Reliability and resiliency design

Agent Framework Fundamentals

• Microsoft Agent Framework concepts
• Agent roles and responsibilities
• Communication patterns

Solution Architecture Best Practices

• Performance optimization
• Security considerations
• Enterprise readiness planning

Build Production-Grade Multi-Agent Capabilities with Microsoft Foundry

Developing Multi-Agent Systems

• Agent creation and configuration
• Agent-to-agent collaboration
• Tool integration strategies

Building Tool Ecosystems

• External service integration
• Workflow orchestration
• Resource management

Implementing Agent Workflows

• Task coordination
• Decision-making processes
• Multi-agent communication

Production Development Practices

• Testing strategies
• Deployment preparation
• Development best practices

Deploy and Govern Enterprise Agentic AI Solutions on Azure

Azure Deployment Strategies

• Enterprise deployment models
• Infrastructure planning
• Scalability implementation

Security and Compliance

• Security architecture
• Identity and access management
• Governance controls

AI Governance Frameworks

• Policy implementation
• Risk management
• Responsible AI practices

Enterprise Operations

• Lifecycle management
• Configuration management
• Change control processes

Monitor, Evaluate, and Operate Multi-Agent AI Solutions in Azure

Monitoring Agent Performance

• Observability practices
• Monitoring frameworks
• Operational dashboards

Evaluating AI Systems

• Performance measurement
• Quality assessment
• Outcome validation

Operational Management

• Incident response
• Troubleshooting techniques
• Service reliability

Optimization and Continuous Improvement

• Performance tuning
• Resource optimization
• Cost management

Enterprise Operations and Maintenance

• Ongoing support models
• Operational governance
• Continuous monitoring practices

Key Competencies Gained

• Multi-agent AI architecture design
• Microsoft Foundry development
• Azure-based deployment strategies
• Tool ecosystem integration
• Agent orchestration and coordination
• Security and governance implementation
• Monitoring and observability
• Enterprise AI operations management