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Duration 14 hours
Course Outline
Foundations of Multi-Agent Systems
- Defining multi-agent systems within the broader AI ecosystem
- Key advantages and potential challenges
- Relevant enterprise use cases and applications
Leveraging AgentCore for Multi-Agent Orchestration
- Understanding the AgentCore orchestration architecture
- Coordinating multiple agents across complex workflows
- Practical exercise: orchestrating basic agent interactions
Collaboration and Communication Frameworks
- Message passing mechanisms and shared memory patterns
- Strategies for negotiation and task distribution
- Practical exercise: establishing agent collaboration protocols
Specialization and Role Allocation
- Designing specialized agents for distinct operational tasks
- Striking a balance between agent autonomy and collective coordination
- Practical exercise: developing role-specific agents
Scaling Multi-Agent Architectures
- Architectural considerations for enterprise-grade scalability
- Techniques for performance monitoring and load balancing
- Practical exercise: scaling an orchestrated agent system
Governance, Security, and Regulatory Compliance
- Ensuring auditability and observability in multi-agent workflows
- Implementing permissioning and security models
- Case study: navigating compliance in regulated industries
Future Trajectories in Multi-Agent AI
- Current trends in autonomous collaboration
- Emerging research on agent collectives
- Strategic implications for enterprise adoption
Conclusion and Recommended Next Steps
Requirements
- Solid comprehension of AI and machine learning system architectures
- Practical experience in distributed system design
- Working knowledge of AWS services and cloud-based architectural patterns
Target Audience
- System architects
- AI researchers
- Enterprise strategy professionals
