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Course Outline
Overview of Managed AI Agents
- Defining AgentCore
- Core features and service offerings
- Cross-industry application examples
Building Your Initial Agent
- Defining agent roles and objectives
- Setting up managed agent configurations
- Practical lab: creating a basic agent
Expanding Agent Capabilities via Memory and Tools
- Incorporating persistence and context
- Connecting external tools and APIs
- Practical lab: enhancing agent functionality
Foundations of AgentCore Runtime and Gateway
- High-level view of runtime architecture
- Gateway integration for software applications
- Practical lab: linking an agent to an application
Rolling Out Managed Agents
- Deployment strategies within AgentCore
- Considerations for scaling and operations
- Practical lab: deploying a fully managed agent
Oversight and Observability
- Utilizing metrics and dashboards in AgentCore
- Monitoring performance and resource usage
- Practical lab: establishing a monitoring pipeline
Optimal Practices and Future Directions
- Governance and compliance frameworks
- Improving usability and system reliability
- Trends in the evolution of managed AI agents
Conclusion and Roadmap
Requirements
- Fundamental knowledge of AI and machine learning principles
- Working familiarity with cloud-based services
- Experience with application development processes
Target Audience
- AI enthusiasts
- Product managers
- Generalist developers
14 Hours