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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 landscape
  • Key advantages and potential challenges
  • Relevant enterprise use cases and practical applications

AgentCore in Multi-Agent Orchestration

  • Understanding the AgentCore orchestration framework
  • Coordinating multiple agents across various workflows
  • Practical exercise: Orchestrating basic agent interactions

Models for Collaboration and Communication

  • Message passing mechanisms and shared memory architectures
  • Strategies for negotiation and task distribution
  • Practical exercise: Developing agent collaboration protocols

Agent Specialization and Role Definition

  • Crafting specialized agents for distinct operational tasks
  • Optimizing the balance between autonomous action and centralized coordination
  • Practical exercise: Developing role-specific agent configurations

Scalability in Multi-Agent Environments

  • Architectural best practices for enterprise-scale deployment
  • Techniques for performance monitoring and load distribution
  • Practical exercise: Scaling an orchestrated agent system

Governance, Security, and Regulatory Compliance

  • Ensuring auditability and observability in multi-agent workflows
  • Implementing permission structures and security frameworks
  • Case study: Navigating compliance in regulated industries

Future Trajectories in Multi-Agent AI

  • Evolving trends in autonomous collaborative behaviors
  • Emerging research in collective agent intelligence
  • Strategic impacts on enterprise adoption strategies

Recap and Recommended Next Steps

Requirements

  • Comprehensive knowledge of AI and machine learning systems
  • Proven experience in distributed system architecture design
  • Proficiency with AWS services and cloud-native architecture principles

Target Audience

  • System architects
  • AI researchers
  • Enterprise strategy teams

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