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Duration 14 hours
Course Outline
Foundations of Multi-Agent Systems
- Defining the role of multi-agent systems within the AI ecosystem.
- Key advantages and inherent challenges.
- Enterprise-specific use cases and real-world applications.
Utilizing AgentCore for Multi-Agent Orchestration
- Understanding the underlying orchestration architecture of AgentCore.
- Coordinating multiple agents throughout complex workflows.
- Practical lab: orchestrating basic agent interactions.
Models for Collaboration and Communication
- Patterns for message passing and shared memory management.
- Strategies for negotiation and task distribution.
- Practical lab: implementing agent collaboration protocols.
Agent Specialization and Role Definition
- Architecting specialized agents for distinct operational tasks.
- Striking a balance between agent autonomy and system coordination.
- Practical lab: developing role-specific agents.
Scaling Multi-Agent Systems
- Architectural considerations for enterprise-level scale.
- Techniques for performance monitoring and load balancing.
- Practical lab: scaling an orchestrated agent system.
Governance, Security, and Compliance
- Ensuring auditability and observability in multi-agent workflows.
- Implementing permissioning frameworks and security models.
- Case study: navigating compliance requirements in regulated industries.
Future Trajectories in Multi-Agent AI
- Current trends in autonomous collaboration.
- Emerging research in agent collectives.
- Strategic implications for enterprise adoption.
Conclusion and Action Plan
Requirements
- A solid grasp of AI and machine learning system architectures.
- Practical experience in designing distributed systems.
- Proficiency with AWS services and cloud-based architectural concepts.
Target Audience
- System architects.
- AI researchers.
- Enterprise strategy teams.
