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Duration 14 hours
Course Outline
Introduction to Multi-Agent Systems
- Defining multi-agent systems within the broader AI ecosystem
- Key benefits and associated challenges
- Relevant enterprise use cases and practical applications
AgentCore for Multi-Agent Orchestration
- Understanding AgentCore's orchestration architecture
- Coordinating multiple agents across complex workflows
- Practical lab: Orchestrating basic agent interactions
Collaboration and Communication Models
- Patterns for message passing and shared memory
- Strategies for negotiation and task allocation
- Practical lab: Implementing agent collaboration protocols
Specialization and Role Assignment
- Designing specialized agents for distinct tasks
- Striking a balance between agent autonomy and system coordination
- Practical lab: Creating role-specific agents
Scaling Multi-Agent Systems
- Architectural considerations for enterprise-scale deployment
- Techniques for performance monitoring and load balancing
- Practical lab: Scaling an orchestrated agent system
Governance, Security, and Compliance
- Ensuring auditability and observability in multi-agent workflows
- Implementing robust permissioning and security models
- Case study: Maintaining compliance in heavily regulated environments
Future Directions in Multi-Agent AI
- Emerging trends in autonomous collaboration
- Current research developments in agent collectives
- Strategic implications for broader enterprise adoption
Summary and Next Steps
Requirements
- Solid grasp of AI and machine learning frameworks
- Practical experience in designing distributed systems
- Working knowledge of AWS services and cloud-native architectures
Target Audience
- System Architects
- AI Researchers
- Enterprise Strategy Teams
