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Duration 14 hours
Course Outline
Foundations of Multi-Agent Systems
- Defining multi-agent systems within the broader AI landscape
- Evaluating core benefits and operational challenges
- Reviewing key enterprise use cases and real-world applications
Orchestrating Multi-Agent Workflows with AgentCore
- Understanding the AgentCore orchestration architecture
- Managing multiple agents across complex workflows
- Hands-on lab: Orchestrate simple agent interactions
Collaboration and Communication Frameworks
- Exploring message passing and shared memory patterns
- Implementing negotiation and task allocation strategies
- Hands-on lab: Build agent collaboration protocols
Role Specialization and Assignment
- Designing specialized agents for distinct functional tasks
- Striking the balance between agent autonomy and system coordination
- Hands-on lab: Develop role-specific agents
Scaling Multi-Agent Architectures
- Key architectural considerations for enterprise-scale deployment
- Techniques for performance monitoring and load balancing
- Hands-on lab: Scale an orchestrated agent system
Governance, Security, and Compliance
- Ensuring auditability and observability in multi-agent workflows
- Implementing robust permissioning and security models
- Case study: Navigating compliance in regulated industries
Future Trends in Multi-Agent AI
- Emerging trends in autonomous agent collaboration
- Current research developments in agent collectives
- Strategic implications for enterprise adoption and planning
Conclusion and Future Actions
Requirements
- Comprehensive knowledge of AI and machine learning systems
- Practical experience in designing distributed systems
- Proficiency with AWS services and cloud-based architectural patterns
Target Audience
- System architects
- AI researchers
- Enterprise strategy teams
