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Course Outline
Introduction to Multi-Agent Systems
- Defining multi-agent systems within the artificial intelligence ecosystem
- Core benefits and challenges of multi-agent systems for government and enterprise environments
- Enterprise use cases and applications for multi-agent systems in public sector operations
AgentCore for Multi-Agent Orchestration
- Overview of AgentCore orchestration architecture for government workflows
- Strategies for managing multiple agents across complex governmental processes
- Hands-on lab: orchestrating simple agent interactions in a controlled environment
Collaboration and Communication Models
- Message passing and shared memory patterns for effective communication among agents
- Negotiation and task allocation strategies to enhance collaboration within multi-agent systems
- Hands-on lab: implementing agent collaboration protocols in a practical scenario
Specialization and Role Assignment
- Designing specialized agents for different tasks within public sector workflows
- Balancing autonomy with coordination to optimize multi-agent performance for government applications
- Hands-on lab: creating role-specific agents tailored for governmental needs
Scaling Multi-Agent Systems
- Architectural considerations for scaling multi-agent systems in large-scale enterprise and public sector environments
- Performance monitoring and load balancing techniques to ensure robust system operation for government use
- Hands-on lab: scaling an orchestrated agent system to meet enterprise-level demands
Governance, Security, and Compliance
- Auditability and observability measures for multi-agent workflows in regulated environments for government
- Permissioning and security models to protect sensitive data and ensure compliance with public sector standards
- Case study: achieving compliance in highly regulated governmental settings
Future Directions in Multi-Agent AI
- Trends in autonomous collaboration within multi-agent systems for government and enterprise adoption
- Emerging research in agent collectives to advance public sector capabilities
- Strategic implications for the broader adoption of multi-agent systems in governmental operations
Summary and Next Steps
Requirements
- Strong understanding of artificial intelligence and machine learning systems for government applications
- Experience with the design of distributed systems to support public sector workflows
- Familiarity with AWS services and cloud-based architectures that align with government governance standards
Audience
- System architects for government projects
- AI researchers focused on government solutions
- Enterprise strategy teams in the public sector
14 Hours
