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Duration 14 hours
Course Outline
Foundations of Multi-Agent Systems
- Defining multi-agent systems within the AI landscape
- Key advantages and associated challenges
- Enterprise applications and use cases
AgentCore in Multi-Agent Orchestration
- Understanding AgentCore’s orchestration architecture
- Coordinating multiple agents across complex workflows
- Practical exercise: orchestrating basic agent interactions
Models for Collaboration and Communication
- Message passing and shared memory mechanisms
- Strategies for negotiation and task distribution
- Practical exercise: implementing agent collaboration protocols
Agent Specialization and Role Definition
- Designing specialized agents for distinct tasks
- Striking a balance between autonomy and coordination
- Practical exercise: developing role-specific agents
Scaling Multi-Agent Architectures
- Architectural considerations for enterprise-level scale
- Performance monitoring and load balancing techniques
- Practical exercise: scaling an orchestrated agent system
Governance, Security, and Compliance
- Ensuring auditability and observability in multi-agent workflows
- Permissioning and security framework models
- Case study: navigating compliance in regulated sectors
Future Trajectories in Multi-Agent AI
- Trends in autonomous collaboration
- Emerging research in agent collectives
- Strategic implications for enterprise integration
Conclusions and Path Forward
Requirements
- Deep understanding of AI and machine learning systems
- Proficiency in distributed system design
- Knowledge of AWS services and cloud-based architectures
Target Audience
- System architects
- AI researchers
- Enterprise strategy teams
