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Duration 14 hours
Course Outline
Introduction to Multi-Agent Systems
- Defining the role of 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 diverse workflows
- Hands-on lab: orchestrating basic agent interactions
Collaboration and Communication Models
- Exploring message passing and shared memory patterns
- Strategies for negotiation and task allocation
- Hands-on lab: implementing agent collaboration protocols
Specialization and Role Assignment
- Designing specialized agents for specific tasks
- Balancing agent autonomy with system 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 in multi-agent workflows
- Implementing permissioning and security models
- Case study: achieving compliance in regulated environments
Future Directions in Multi-Agent AI
- Emerging trends in autonomous collaboration
- New research in agent collectives
- Strategic implications for enterprise adoption
Summary and Next Steps
Requirements
- A solid grasp of AI and machine learning systems
- Practical experience with distributed system design
- Familiarity with AWS services and cloud-based architectures
Audience
- System architects
- AI researchers
- Enterprise strategy teams
