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Duration 14 hours
Course Outline
Foundations of Multi-Agent Systems
- Defining the role of multi-agent systems within the broader AI landscape
- Key advantages and potential challenges
- Relevant enterprise use cases and practical applications
AgentCore in Multi-Agent Orchestration
- Understanding the AgentCore orchestration framework
- Coordinating multiple agents across various workflows
- Practical exercise: Orchestrating basic agent interactions
Models for Collaboration and Communication
- Message passing mechanisms and shared memory architectures
- Strategies for negotiation and task distribution
- Practical exercise: Developing agent collaboration protocols
Agent Specialization and Role Definition
- Crafting specialized agents for distinct operational tasks
- Optimizing the balance between autonomous action and centralized coordination
- Practical exercise: Developing role-specific agent configurations
Scalability in Multi-Agent Environments
- Architectural best practices for enterprise-scale deployment
- Techniques for performance monitoring and load distribution
- Practical exercise: Scaling an orchestrated agent system
Governance, Security, and Regulatory Compliance
- Ensuring auditability and observability in multi-agent workflows
- Implementing permission structures and security frameworks
- Case study: Navigating compliance in regulated industries
Future Trajectories in Multi-Agent AI
- Evolving trends in autonomous collaborative behaviors
- Emerging research in collective agent intelligence
- Strategic impacts on enterprise adoption strategies
Recap and Recommended Next Steps
Requirements
- Comprehensive knowledge of AI and machine learning systems
- Proven experience in distributed system architecture design
- Proficiency with AWS services and cloud-native architecture principles
Target Audience
- System architects
- AI researchers
- Enterprise strategy teams