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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 ecosystem.
- Identifying key advantages and addressing inherent challenges.
- Exploring real-world enterprise use cases and applications.
Orchestrating Multi-Agents with AgentCore
- Examining the underlying orchestration architecture of AgentCore.
- Coordinating multiple agents across complex workflows.
- Practical lab: Orchestrating basic agent interactions.
Models for Collaboration and Communication
- Utilizing message passing and shared memory design patterns.
- Developing negotiation and task allocation strategies.
- Practical lab: Implementing agent collaboration protocols.
Specialization and Role Assignment
- Crafting specialized agents tailored to distinct tasks.
- Striking a balance between individual autonomy and collective coordination.
- Practical lab: Developing role-specific agents.
Scaling Multi-Agent Architectures
- Addressing architectural considerations for enterprise-level scale.
- Implementing performance monitoring and load balancing techniques.
- Practical lab: Expanding the capacity of an orchestrated agent system.
Governance, Security, and Compliance
- Ensuring auditability and observability in multi-agent workflows.
- Implementing robust permissioning and security models.
- Case study: Navigating compliance in highly regulated environments.
Future Trajectories in Multi-Agent AI
- Monitoring trends in autonomous collaboration.
- Reviewing emerging research on agent collectives.
- Evaluating strategic implications for enterprise adoption.
Conclusion and Path Forward
Requirements
- A solid grasp of AI and machine learning system dynamics.
- Practical experience in designing distributed systems.
- Proficiency with AWS services and cloud-based architectural models.
Target Audience
- System Architects.
- AI Researchers.
- Enterprise Strategy Teams.
