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Duration 14 hours
Course Outline
Introduction to Multi-Agent Systems
- Defining multi-agent systems within the AI landscape
- Key advantages and inherent challenges
- Enterprise applications and use cases
AgentCore for Multi-Agent Orchestration
- AgentCore’s orchestration architecture
- Coordinating multiple agents across 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 distribution
- Hands-on lab: Implementing protocols for agent collaboration
Specialization and Role Assignment
- Developing specialized agents for distinct tasks
- Striking a balance between autonomy and coordination
- Hands-on lab: Creating agents with specific roles
Scaling Multi-Agent Systems
- Architectural considerations for enterprise-level scaling
- Performance monitoring and load balancing techniques
- Hands-on lab: Scaling an orchestrated agent system
Governance, Security, and Compliance
- Auditability and observability in multi-agent workflows
- Permission models and security frameworks
- Case study: Ensuring compliance in regulated industries
Future Trends in Multi-Agent AI
- Trends in autonomous collaboration
- Emerging research on agent collectives
- Strategic impacts on enterprise adoption
Summary and Next Steps
Requirements
- A robust understanding of AI and machine learning systems
- Practical experience in distributed system design
- Knowledge of AWS services and cloud-based architectures
Target Audience
- System architects
- AI researchers
- Enterprise strategy teams
