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Duration 14 hours
Course Outline
Foundations of Multi-Agent Systems
- Defining multi-agent systems within the broader AI landscape
- Key advantages and associated complexities
- Real-world enterprise applications and use cases
Utilizing AgentCore for Multi-Agent Coordination
- AgentCore orchestration architecture components
- Overseeing multiple agents throughout complex workflows
- Practical exercise: Coordinating basic agent interactions
Models for Collaboration and Communication
- Patterns for message passing and shared memory
- Strategies for negotiation and task distribution
- Practical exercise: Building agent collaboration protocols
Agent Specialization and Role Definition
- Crafting specialized agents for distinct operational tasks
- Striking a balance between autonomy and collective coordination
- Practical exercise: Developing role-specific agents
Scaling Multi-Agent Architectures
- Architectural factors for enterprise-level scaling
- Performance monitoring and load balancing techniques
- Practical exercise: Expanding an orchestrated agent system
Governance, Security, and Regulatory Compliance
- Ensuring auditability and observability in multi-agent workflows
- Frameworks for permissions and security controls
- Case analysis: Compliance in regulated sectors
Future Trajectories in Multi-Agent AI
- Evolving trends in autonomous collaboration
- New research directions in agent collectives
- Strategic considerations for enterprise adoption
Conclusion and Subsequent Actions
Requirements
- Comprehensive knowledge of AI and machine learning systems
- Proven experience in distributed system design
- Proficiency with AWS services and cloud-native architectures
Target Audience
- System architects
- AI researchers
- Enterprise strategy teams
