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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 challenges
- Enterprise-specific use cases and practical applications
Leveraging AgentCore for Multi-Agent Orchestration
- Understanding the orchestration architecture of AgentCore
- Coordinating multiple agents across various workflows
- Practical exercise: Managing basic agent interactions
Models for Collaboration and Communication
- Techniques for message passing and shared memory usage
- Strategies for negotiation and task distribution
- Practical exercise: Establishing agent collaboration protocols
Role Specialization and Assignment
- Creating specialized agents for distinct tasks
- Striking a balance between agent autonomy and system coordination
- Practical exercise: Developing role-specific agent profiles
Scaling Multi-Agent Architectures
- Architectural best practices for enterprise-level scale
- 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
- Implementing permission structures and security models
- Case study: Navigating compliance in heavily regulated sectors
Future Trajectories in Multi-Agent AI
- Emerging trends in autonomous agent collaboration
- New research directions in agent collectives
- Strategic impacts on enterprise adoption strategies
Wrap-up and Recommended Next Steps
Requirements
- Solid comprehension of AI and machine learning frameworks
- Proficiency in designing distributed systems
- Knowledge of AWS services and cloud-based architectural patterns
Target Audience
- System architects
- AI researchers
- Enterprise strategy teams
