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Duration 14 hours
Course Outline
Foundations of Multi-Agent Systems
- Defining the role of multi-agent systems within the AI landscape
- Key advantages and potential challenges
- Practical enterprise applications and use cases
Leveraging AgentCore for Multi-Agent Coordination
- AgentCore orchestration framework overview
- Coordinating multiple agents across complex workflows
- Practical exercise: orchestrating basic agent interactions
Models for Collaboration and Communication
- Message passing mechanisms and shared memory techniques
- Strategies for negotiation and task distribution
- Practical exercise: building agent collaboration protocols
Agent Specialization and Role Definition
- Creating specialized agents for distinct operational tasks
- Striking a balance between individual autonomy and group coordination
- Practical exercise: developing role-specific agents
Scaling Multi-Agent Architectures
- Architectural best practices for enterprise-scale deployments
- Techniques for performance monitoring and load distribution
- Practical exercise: expanding an orchestrated agent system
Governance, Security, and Regulatory Compliance
- Ensuring auditability and observability in multi-agent workflows
- Implementing robust permissioning and security models
- Case study: maintaining compliance in regulated sectors
Trajectories in Multi-Agent AI
- Emerging trends in autonomous collaborative AI
- Current research developments in agent collectives
- Strategic considerations for enterprise integration
Conclusions and Recommended Pathways
Requirements
- Comprehensive knowledge of AI and machine learning frameworks
- Proficiency in designing distributed systems
- Working familiarity with AWS services and cloud-native architectures
Target Audience
- System architects
- AI researchers
- Enterprise strategic planning teams
