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Course Outline

Introduction to AgentCore and Agentic AI

  • The role of Agentic AI in enterprise environments
  • Key components of the AgentCore platform
  • How AgentCore fits into the broader AWS Bedrock ecosystem

AgentCore Runtime and Gateway

  • Configuring the AgentCore Runtime environment
  • Integrating secure APIs via Gateway
  • Practical exercise: deploying a sample AI agent

Memory and Stateful Agents

  • Establishing persistent context for agents
  • Architecting workflows for long-running agent tasks
  • Practical exercise: implementing session-based memory

Identity, Permissions, and Security

  • Defining role-based access controls for AI agents
  • Managing identity federation and enterprise integrations
  • Practical exercise: setting up agent permission structures

Observability and Monitoring

  • Utilizing logging and tracing features in AgentCore
  • Tracking metrics for usage and performance
  • Practical exercise: creating observability dashboards

Scaling and Orchestrating Multi-Agent Systems

  • Design patterns for coordinating multiple agents
  • Strategies for performance optimization and reliability
  • Practical exercise: orchestrating specialized agents

Governance and Compliance

  • Ensuring auditability and safe large-scale rollouts
  • Compliance frameworks available within AWS
  • Best practices for industries with strict regulatory requirements

Course Summary and Recommendations for Next Steps

Requirements

  • Familiarity with cloud-based AI and ML services
  • Practical experience with tools within the AWS ecosystem
  • Understanding of enterprise-level security and observability principles

Target Audience

  • AI and ML Engineers
  • DevOps Leaders
  • Solution Architects
 14 Hours

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