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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 associated challenges
- Relevant enterprise use cases and practical applications
Leveraging AgentCore for Multi-Agent Orchestration
- Exploring the orchestration architecture of AgentCore
- Coordinating multiple agents across complex workflows
- Practical exercise: Orchestrating basic agent interactions
Models for Collaboration and Communication
- Patterns for message passing and shared memory management
- Strategies for negotiation and task distribution
- Practical exercise: Implementing agent collaboration protocols
Agent Specialization and Role Definition
- Designing agents tailored for specific functional tasks
- Striking a balance between agent autonomy and system coordination
- Practical exercise: Creating agents with defined roles
Scaling Multi-Agent Architectures
- Architectural considerations for enterprise-level scalability
- Techniques for performance monitoring and load distribution
- Practical exercise: Scaling an orchestrated agent environment
Governance, Security, and Compliance Frameworks
- Ensuring auditability and observability in multi-agent processes
- Implementing permissioning structures and security models
- Case study: Navigating compliance requirements in regulated sectors
Future Trajectories in Multi-Agent AI
- Evolving trends in autonomous collaboration
- New research directions in agent collectives
- Strategic insights for enterprise integration
Conclusions and Path Forward
Requirements
- A solid command of AI and machine learning system dynamics
- Practical experience in designing distributed systems
- Proficiency with AWS services and cloud-native architectural patterns
Target Audience
- System Architects
- AI Researchers
- Enterprise Strategy Professionals
