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Duration 14 hours
Course Outline
Foundations of Multi-Agent Systems
- Defining multi-agent paradigms within the broader AI landscape
- Key advantages and operational challenges
- Applications in enterprise settings
Leveraging AgentCore for Orchestration
- The architectural design of AgentCore orchestration
- Coordinating multiple agents across complex workflows
- Practical lab: Coordinating basic agent interactions
Models for Collaboration and Communication
- Patterns for message exchange and shared memory
- Strategies for negotiation and task distribution
- Practical lab: Building protocols for agent cooperation
Specialization and Role Definition
- Crafting specialized agents for distinct tasks
- Striking a balance between autonomy and collective coordination
- Practical lab: Developing role-specific agents
Scaling Multi-Agent Architectures
- Architectural considerations for enterprise-level scale
- Monitoring performance and managing load distribution
- Practical lab: Scaling an orchestrated agent network
Governance, Security, and Regulatory Compliance
- Ensuring auditability and observability in multi-agent flows
- Implementing permissioning structures and security models
- Case study: Meeting compliance standards in regulated sectors
The Future of Multi-Agent AI
- Developing trends in autonomous collaboration
- New research directions in agent collectives
- Strategic considerations for enterprise adoption
Conclusion and Forward Path
Requirements
- A solid command of AI and machine learning frameworks
- Proficiency in designing distributed systems
- Knowledge of AWS services and cloud-native architectures
Target Audience
- System architects
- AI researchers
- Enterprise strategic teams
