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Course Outline
Introduction to Multi-Agent Systems
- Defining multi-agent systems within the AI ecosystem
- Key benefits and associated challenges
- Enterprise use cases and practical applications
AgentCore for Multi-Agent Orchestration
- Understanding AgentCore orchestration architecture
- Managing multiple agents across complex workflows
- Hands-on lab: orchestrating simple agent interactions
Collaboration and Communication Models
- Exploring message passing and shared memory patterns
- Negotiation techniques and task allocation strategies
- Hands-on lab: implementing agent collaboration protocols
Specialization and Role Assignment
- Designing specialized agents for specific tasks
- Balancing autonomy with effective coordination
- Hands-on lab: creating role-specific agents
Scaling Multi-Agent Systems
- Architectural considerations for enterprise-scale deployment
- Performance monitoring and load balancing techniques
- Hands-on lab: scaling an orchestrated agent system
Governance, Security, and Compliance
- Ensuring auditability and observability for multi-agent workflows
- Defining permissioning and security models
- Case study: ensuring compliance in regulated environments
Future Directions in Multi-Agent AI
- Trends in autonomous collaboration
- Emerging research in agent collectives
- Strategic implications for enterprise adoption
Summary and Next Steps
Requirements
- Comprehensive understanding of AI and machine learning systems
- Practical experience in distributed system design
- Familiarity with AWS services and cloud-based architectures
Target Audience
- System architects
- AI researchers
- Enterprise strategy teams
14 Hours
