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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
- Enterprise use cases and practical applications
Utilising AgentCore for Multi-Agent Orchestration
- The architectural underpinnings of AgentCore orchestration
- Managing multiple agents across various workflows
- Hands-on lab: orchestrating basic agent interactions
Models for Collaboration and Communication
- Message passing mechanisms and shared memory patterns
- Strategies for negotiation and task allocation
- Hands-on lab: implementing agent collaboration protocols
Agent Specialisation and Role Assignment
- Designing specialised agents for distinct tasks
- Striking a balance between autonomy and coordination
- Hands-on lab: creating role-specific agents
Scaling Multi-Agent Systems
- Architectural considerations for enterprise-scale deployments
- 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
- Permissioning frameworks and security models
- Case study: navigating compliance in regulated environments
Future Directions in Multi-Agent AI
- Emerging trends in autonomous collaboration
- Current research into agent collectives
- Strategic implications for enterprise adoption
Summary and Next Steps
Requirements
- A robust grasp of AI and machine learning systems
- Practical experience in distributed system design
- Familiarity with AWS services and cloud-based architectures
Target Audience
- System architects
- AI researchers
- Enterprise strategy teams
