Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 14 hours
Course Outline
Foundations of Multi-Agent Systems
- Defining the role of multi-agent systems within the AI ecosystem.
- Evaluating core benefits and associated challenges.
- Exploring enterprise use cases and practical applications.
AgentCore in Multi-Agent Orchestration
- Understanding the orchestration architecture of AgentCore.
- Coordinating multiple agents across various workflows.
- Practical lab: Managing simple agent interactions.
Models for Collaboration and Communication
- Utilizing message passing and shared memory patterns.
- Strategies for negotiation and task allocation.
- Practical lab: Implementing protocols for agent collaboration.
Specialization and Role Definition
- Creating specialized agents tailored to specific tasks.
- Striking a balance between autonomy and coordination.
- Practical lab: Developing role-specific agents.
Scaling Multi-Agent Architectures
- Architectural strategies for enterprise-scale deployments.
- 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 and security models.
- Case study: Meeting compliance standards in regulated environments.
Future Trajectories in Multi-Agent AI
- Observing trends in autonomous collaboration.
- Reviewing emerging research on agent collectives.
- Assessing strategic implications for enterprise adoption.
Recap and Future Steps
Requirements
- A solid grasp of AI and machine learning system concepts.
- Proven experience in distributed system design.
- Knowledge of AWS services and cloud-native architectures.
Target Audience
- System architects.
- AI researchers.
- Enterprise strategy teams.
