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Duration 14 hours
Course Outline
Introduction to Multi-Agent Systems
- Defining the role of multi-agent systems within the AI ecosystem
- Key advantages and potential challenges
- Enterprise-level use cases and practical applications
AgentCore for Multi-Agent Orchestration
- Overview of the AgentCore orchestration architecture
- Managing multiple agents throughout various workflows
- Hands-on lab: orchestrating basic agent interactions
Collaboration and Communication Models
- Patterns for message passing and shared memory
- Strategies for negotiation and task allocation
- Hands-on lab: implementing protocols for agent collaboration
Specialization and Role Assignment
- Designing specialized agents for specific task requirements
- Balancing individual autonomy with system-wide coordination
- Hands-on lab: creating agents with defined roles
Scaling Multi-Agent Systems
- Architectural considerations for enterprise-scale deployment
- Techniques for performance monitoring and load balancing
- Hands-on 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 industries
Future Directions in Multi-Agent AI
- Evolving trends in autonomous collaboration
- New research areas in agent collectives
- Strategic impact on enterprise AI adoption
Summary and Next Steps
Requirements
- A solid grasp of AI and machine learning systems
- Practical experience in designing distributed systems
- Knowledge of AWS services and cloud-native architectures
Target Audience
- System architects
- AI researchers
- Enterprise strategy teams
