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Duration 14 hours
Course Outline
Fundamentals of Multi-Agent Systems
- Defining the role of multi-agent systems within the AI landscape
- Key advantages and associated challenges
- Real-world enterprise use cases and applications
Leveraging AgentCore for Multi-Agent Orchestration
- Understanding the AgentCore orchestration architecture
- Coordinating multiple agents across complex workflows
- Practical exercise: Managing basic agent interactions
Models for Collaboration and Communication
- Exploring message passing and shared memory mechanisms
- Strategies for negotiation and task distribution
- Practical exercise: Building agent collaboration protocols
Agent Specialization and Role Definition
- Building specialized agents for distinct tasks
- Striking a balance between autonomy and coordination
- Practical exercise: Developing role-based agents
Scaling Multi-Agent Ecosystems
- Architectural strategies for enterprise-level scalability
- Performance tracking and load distribution techniques
- Practical exercise: Expanding an orchestrated agent system
Governance, Security, and Regulatory Compliance
- Ensuring auditability and observability in multi-agent flows
- Implementing permission structures and security frameworks
- Case analysis: Meeting compliance standards in regulated sectors
Future Trends in Multi-Agent AI
- Developing trends in autonomous cooperation
- New research frontiers in agent collectives
- Strategic impacts on enterprise adoption strategies
Conclusion and Recommended Path Forward
Requirements
- A solid grasp of AI and machine learning frameworks.
- Practical experience in designing distributed systems.
- Knowledge of AWS services and cloud-native architectures.
Target Audience
- System architects.
- AI researchers.
- Enterprise strategic teams.
