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Duration 14 hours
Course Outline
Foundations of Multi-Agent Systems
- Defining the role of multi-agent systems within the broader AI ecosystem
- Key benefits and associated challenges
- Real-world enterprise use cases and applications
Leveraging AgentCore for Multi-Agent Orchestration
- Understanding the AgentCore orchestration architecture
- Overseeing multiple agents across complex workflows
- Hands-on lab: Orchestrating basic agent interactions
Models for Collaboration and Communication
- Patterns for message passing and shared memory
- Strategies for negotiation and task allocation
- Hands-on lab: Implementing protocols for agent collaboration
Specialization and Role Allocation
- Developing specialized agents for specific tasks
- Striking a balance between autonomy and coordination
- Hands-on lab: Building role-specific agents
Scaling Multi-Agent Architectures
- 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
- Permission structures and security models
- Case study: Maintaining compliance in regulated environments
Future Trajectories in Multi-Agent AI
- Current trends in autonomous collaboration
- Emerging research on agent collectives
- Strategic implications for enterprise adoption
Conclusion and Path Forward
Requirements
- A solid grasp of AI and machine learning systems
- Practical experience in designing distributed systems
- Proficiency with AWS services and cloud-native architectures
Target Audience
- System Architects
- AI Researchers
- Enterprise Strategy Teams
