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Course Outline
Introduction to Managed AI Agents
- Defining AgentCore
- Core features and services
- Industry-specific use cases
Designing 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
- Implementing persistence and context management
- Connecting tools and APIs
- Practical lab: expanding agent capabilities
AgentCore Runtime and Gateway Fundamentals
- Overview of runtime architecture
- Gateway integration for applications
- Practical lab: linking an agent to an application
Deploying Managed Agents
- Deployment strategies within AgentCore
- Scaling and operational factors
- Practical lab: launching a fully managed agent
Monitoring and Observability
- Utilizing metrics and dashboards in AgentCore
- Tracking performance and usage patterns
- Practical lab: creating a monitoring workflow
Best Practices and Emerging Trends
- Governance and compliance factors
- Optimizing for usability and reliability
- Future directions in managed AI agents
Summary and Next Steps
Requirements
- Foundational knowledge of AI and machine learning principles
- Acquaintance with cloud service ecosystems
- Exposure to standard application development workflows
Target Audience
- AI enthusiasts
- Product managers
- Generalist developers
14 Hours