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Duration 14 hours
Course Outline
Fundamentals of Managed AI Agents
- Defining AgentCore
- Primary features and service offerings
- Industry-specific application examples
Architecting Your Initial Agent
- Defining agent roles and objectives
- Setting up managed agent parameters
- Practical lab: constructing a basic agent
Augmenting Agents with Memory and Tools
- Incorporating persistence and contextual data
- Connecting external tools and APIs
- Practical lab: expanding agent functionalities
Understanding AgentCore Runtime and Gateway
- Overview of runtime architecture
- Gateway integration for application support
- Practical lab: linking an agent to an application
Releasing Managed Agents
- Deployment methods within AgentCore
- Considerations for scaling and operations
- Practical lab: releasing a fully managed agent
Monitoring and Observability Strategies
- Utilizing metrics and dashboards in AgentCore
- Monitoring performance and usage patterns
- Practical lab: creating a monitoring workflow
Best Practices and Industry Trends
- Compliance and governance frameworks
- Strategies for improving usability and reliability
- Future developments in managed AI agents
Conclusion and Recommended Next Steps
Requirements
- Fundamental knowledge of AI and machine learning principles
- General awareness of cloud service environments
- Previous exposure to application development processes
Target Participants
- AI enthusiasts
- Product managers
- Generalist software developers
