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Course Outline
Introduction to Multi-Agent Systems
- Defining multi-agent systems within the AI ecosystem.
- Key benefits and inherent challenges.
- Enterprise use cases and practical applications.
AgentCore for Multi-Agent Orchestration
- Exploring the AgentCore orchestration architecture.
- Managing multiple agents across complex workflows.
- Practical lab: Orchestrating simple agent interactions.
Collaboration and Communication Models
- Message passing and shared memory patterns.
- Strategies for negotiation and task allocation.
- Practical lab: Implementing agent collaboration protocols.
Specialization and Role Assignment
- Designing specialized agents tailored to specific tasks.
- Striking a balance between autonomy and coordination.
- Practical lab: Creating role-specific agents.
Scaling Multi-Agent Systems
- Architectural considerations for enterprise-scale deployment.
- Performance monitoring and load balancing techniques.
- Practical lab: Scaling an orchestrated agent system.
Governance, Security, and Compliance
- Ensuring auditability and observability for multi-agent workflows.
- Implementing permissioning and security models.
- Case study: Navigating compliance in regulated environments.
Future Directions in Multi-Agent AI
- Current trends in autonomous collaboration.
- Emerging research areas in agent collectives.
- Strategic implications for enterprise adoption.
Summary and Next Steps
Requirements
- Comprehensive understanding 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.
14 Hours
