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Duration 14 hours
Course Outline
Foundations of Multi-Agent Systems
- Defining multi-agent systems within the broader AI landscape.
- Exploring key advantages and common challenges.
- Reviewing relevant enterprise use cases and applications.
Orchestrating Multi-Agent Systems with AgentCore
- Understanding the AgentCore orchestration architecture.
- Managing multiple agents across complex workflows.
- Practical exercise: Orchestrating basic agent interactions.
Models for Collaboration and Communication
- Implementing message passing and shared memory patterns.
- Strategies for negotiation and task distribution.
- Practical exercise: Establishing agent collaboration protocols.
Agent Specialization and Role Definition
- Designing specialized agents for specific tasks.
- Balancing agent autonomy with collective coordination.
- Practical exercise: Building role-specific agents.
Scaling Multi-Agent Architectures
- Architectural best practices for enterprise-level scale.
- Techniques for performance monitoring and load balancing.
- Practical exercise: Scaling an orchestrated agent system.
Governance, Security, and Compliance
- Ensuring auditability and observability in multi-agent workflows.
- Implementing permissioning and security frameworks.
- Case study: Maintaining compliance in regulated industries.
The Future of Multi-Agent AI
- Current trends in autonomous collaboration.
- Emerging research in agent collectives.
- Strategic considerations for enterprise adoption.
Conclusion and Path Forward
Requirements
- A solid command of AI and machine learning system concepts.
- Practical experience in designing distributed systems.
- A working familiarity with AWS services and cloud-native architectures.
Intended Audience
- System architects.
- AI researchers.
- Enterprise strategy and leadership teams.
