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Duration 14 hours
Course Outline
Foundations of Multi-Agent Systems
- Defining the role of multi-agent systems within the AI ecosystem
- Key advantages and potential challenges
- Real-world enterprise use cases and applications
Leveraging AgentCore for Orchestration
- Overview of the AgentCore orchestration architecture
- Coordinating multiple agents across various workflows
- Practical lab: Orchestrating basic agent interactions
Models for Collaboration and Communication
- Techniques for message passing and shared memory
- Strategies for negotiation and task allocation
- Practical lab: Implementing agent collaboration protocols
Agent Specialization and Role Definition
- Designing specialized agents for distinct tasks
- Balancing agent autonomy with overall coordination
- Practical lab: Developing role-specific agents
Scaling Multi-Agent Architectures
- Architectural considerations for enterprise-scale deployments
- Performance monitoring and load balancing techniques
- Practical 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: Maintaining compliance in regulated environments
Future Trends in Multi-Agent AI
- Evolving trends in autonomous collaboration
- Emerging research in agent collectives
- Strategic implications for enterprise adoption
Conclusion and Path Forward
Requirements
- A solid grasp of AI and machine learning frameworks
- Practical experience in distributed system design
- Proficiency with AWS services and cloud-based architectures
Target Audience
- System Architects
- AI Researchers
- Enterprise Strategy Teams
