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Course Outline
Introduction to Managed AI Agents
- Defining AgentCore
- Core features and available services
- Industry-specific use cases
Creating Your First Agent
- Defining agent roles and objectives
- Setting up managed agent parameters
- Practical lab: Constructing a basic agent
Expanding Agent Capabilities with Memory and Tools
- Incorporating persistence and contextual awareness
- Connecting external tools and APIs
- Practical lab: Broadening agent functionality
Foundations of AgentCore Runtime and Gateway
- Overview of runtime architecture
- Gateway integration for application connectivity
- Practical lab: Linking an agent to an application
Releasing Managed Agents
- Deployment strategies within AgentCore
- Scalability and operational factors
- Practical lab: Launching a fully managed agent
Observability and Monitoring
- Utilizing metrics and dashboards in AgentCore
- Monitoring performance and consumption
- Practical lab: Creating a monitoring pipeline
Best Practices and Future Directions
- Governance and regulatory compliance
- Enhancing usability and system reliability
- Trends in the evolution of managed AI agents
Conclusion and Next Steps
Requirements
- Foundational knowledge of AI and machine learning principles
- Awareness of cloud computing services
- Exposure to standard application development processes
Target Participants
- AI professionals
- Product managers
- Generalist developers
14 Hours