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Duration 14 hours
Course Outline
Foundations of Multi-Agent Systems
- Defining multi-agent systems within the broader AI ecosystem
- Key advantages and inherent challenges
- Relevant enterprise use cases and application scenarios
AgentCore for Multi-Agent Orchestration
- Architecture of AgentCore orchestration
- Oversight and management of multiple agents across workflows
- Practical lab: Orchestrating basic agent interactions
Models for Collaboration and Communication
- Message passing mechanisms and shared memory patterns
- Strategies for negotiation and task distribution
- Practical lab: Implementing agent collaboration protocols
Specialization and Role Definition
- Designing specialized agents for distinct functional tasks
- Optimizing the balance between autonomy and coordination
- Practical lab: Creating role-specific agents
Scaling Multi-Agent Systems
- Architectural best practices for enterprise-scale deployment
- Performance monitoring and load balancing techniques
- Practical lab: Scaling an orchestrated agent system
Governance, Security, and Compliance
- Ensuring auditability and observability in multi-agent workflows
- Implementing permissioning structures and security models
- Case study: Maintaining compliance in regulated industries
Future Trajectories in Multi-Agent AI
- Evolving trends in autonomous collaboration
- Emerging research on agent collectives
- Strategic implications for enterprise adoption
Wrap-up and Recommendations
Requirements
- Comprehensive understanding of AI and machine learning system design.
- Proven experience in distributed system architecture.
- Working knowledge of AWS services and cloud-native architectures.
Target Audience
- System Architects
- AI Researchers
- Enterprise Strategy Teams
