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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