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Course Outline
Introduction to Multi-Agent Systems
- Defining multi-agent systems within the AI landscape.
- Key benefits and associated challenges.
- Enterprise use cases and practical applications.
Leveraging AgentCore for Multi-Agent Orchestration
- Overview of AgentCore orchestration architecture.
- Managing multiple agents across complex workflows.
- Hands-on lab: Orchestrating basic agent interactions.
Collaboration and Communication Models
- Implementing message passing and shared memory patterns.
- Strategies for negotiation and task allocation.
- Hands-on lab: Implementing agent collaboration protocols.
Specialisation and Role Assignment
- Designing specialised agents for distinct tasks.
- Striking a balance between autonomy and coordination.
- Hands-on lab: Creating role-specific agents.
Scaling Multi-Agent Systems
- Architectural considerations for enterprise-scale deployments.
- Performance monitoring and load balancing techniques.
- Hands-on lab: Scaling an orchestrated agent system.
Governance, Security, and Compliance
- Ensuring auditability and observability for multi-agent workflows.
- Establishing permissioning and security models.
- Case study: Navigating compliance in regulated environments.
Future Directions in Multi-Agent AI
- Emerging trends in autonomous collaboration.
- Current research into agent collectives.
- Strategic implications for enterprise adoption.
Summary and Next Steps
Requirements
- Robust understanding of AI and machine learning systems.
- Practical experience in distributed system design.
- Familiarity with AWS services and cloud-based architectures.
Target Audience
- System architects.
- AI researchers.
- Enterprise strategy teams.
14 Hours
