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Duration 14 hours
Course Outline
Overview of AgentCore and Agentic AI
- The role of Agentic AI in enterprise environments
- Essential components of the AgentCore framework
- Integration within the broader AWS Bedrock ecosystem
AgentCore Runtime and Gateway Mechanics
- Initializing and configuring the AgentCore Runtime
- Achieving secure API integrations via Gateway
- Lab exercise: launching and testing a sample agent
Memory Management and Stateful Agents
- Establishing persistent context for AI agents
- Structuring workflows for long-running agent operations
- Lab exercise: activating session-based memory features
Identity, Access Control, and Security
- Implementing role-based access strategies for AI agents
- Managing identity federation and enterprise-level integrations
- Lab exercise: setting and verifying agent permissions
Observability and Performance Monitoring
- Utilizing logging and tracing features within AgentCore
- Tracking usage patterns and performance metrics
- Lab exercise: creating comprehensive observability dashboards
Scaling and Orchestrating Multi-Agent Systems
- Applying design patterns for effective multi-agent collaboration
- Optimizing performance and ensuring system reliability
- Lab exercise: orchestrating a network of specialized agents
Governance and Regulatory Compliance
- Ensuring auditability and safe large-scale rollouts
- Navigating compliance frameworks supported by AWS
- Adopting best practices for highly regulated sectors
Conclusion and Future Directions
Requirements
- Familiarity with cloud-based AI and ML services
- Hands-on experience with the AWS ecosystem and its tools
- Knowledge of enterprise security standards and observability principles
Target Audience
- AI/ML Engineers
- DevOps Leads
- Solution Architects
