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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
- Evaluating key advantages and inherent complexities
- Exploring practical enterprise applications and use cases
AgentCore for Orchestration
- Analyzing the AgentCore orchestration architecture
- Coordinating multiple agents across complex workflows
- Practical exercise: Coordinating basic agent interactions
Collaborative Communication Frameworks
- Examining message passing and shared memory mechanisms
- Strategies for negotiation and task distribution
- Practical exercise: Developing agent collaboration protocols
Specialization and Role Definition
- Architecting specialized agents for distinct functional tasks
- Achieving the balance between autonomy and centralized coordination
- Practical exercise: Configuring role-based agent behaviors
Scaling for Enterprise Environments
- Architectural requirements for large-scale enterprise deployment
- Techniques for performance monitoring and load distribution
- Practical exercise: Expanding an orchestrated agent system
Governance, Security, and Regulatory Compliance
- Ensuring audit trails and observability in multi-agent workflows
- Implementing permission structures and security frameworks
- Case analysis: Navigating compliance in highly regulated sectors
Future Trajectories in Multi-Agent AI
- Current trends in autonomous inter-agent collaboration
- Frontier research in collective agent behaviors
- Strategic outlook for enterprise adoption and integration
Conclusions and Strategic Roadmap
Requirements
- Comprehensive proficiency in AI and machine learning frameworks.
- Proven experience in distributed system architecture.
- Working knowledge of AWS services and cloud-native infrastructure.
Target Audience
- System Architects
- AI Research Professionals
- Enterprise Strategic Planning Teams
