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Course Outline
Foundations of Multi-Agent Systems
- Defining multi-agent systems within the broader AI landscape
- Key advantages and associated challenges
- Applicable enterprise scenarios and use cases
Leveraging AgentCore for Multi-Agent Orchestration
- Understanding the AgentCore orchestration architecture
- Coordinating multiple agents throughout complex workflows
- Practical lab: orchestrating basic agent interactions
Models for Collaboration and Communication
- Techniques for message passing and shared memory
- Strategies for negotiation and task distribution
- Practical lab: building agent collaboration protocols
Specialization and Role Assignment
- Developing specialized agents for distinct tasks
- Striking a balance between autonomy and coordination
- Practical lab: creating agents with specific roles
Scaling Multi-Agent Ecosystems
- Architectural requirements for enterprise-scale deployment
- Techniques for performance monitoring and load balancing
- Practical lab: scaling a coordinated agent system
Governance, Security, and Compliance
- Ensuring auditability and observability in multi-agent workflows
- Implementing permissioning and security frameworks
- Case study: maintaining compliance in regulated sectors
Future Trajectories in Multi-Agent AI
- Developing trends in autonomous collaboration
- Emerging research in agent collectives
- Strategic implications for corporate adoption
Recap and Subsequent Steps
Requirements
- A solid command of AI and machine learning systems
- Practical experience in designing distributed systems
- Proficiency with AWS services and cloud-based architectures
Target Audience
- System architects
- AI researchers
- Enterprise strategy teams
14 Hours
