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Duration 14 hours
Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore's interactive capabilities
- Designing rich workflows utilizing memory and tools
- Applicable use cases in analytics, automation, and support
Managing AgentCore Memory
- Configuring session persistence
- Designing multi-step, context-aware workflows
- Practical lab: Constructing a data analysis agent with memory capabilities
Dynamic Computation via the Code Interpreter
- Reviewing supported operations and security boundaries
- Safely executing transformations and calculations
- Practical lab: Implementing real-time data transformations
Real-Time Engagement with the Browser Tool
- Setup of the browser tool for agent workflows
- Techniques for data retrieval and user interface interactions
- Practical lab: Creating an agent with web interaction features
Integrating Memory, Code, and Browser Tools
- Linking workflows across memory components and tools
- Architecting multi-modal, interactive workflows
- Practical lab: Developing a customer support assistant
Testing and Observability
- Debugging interactive workflow processes
- Implementing logging and monitoring for tool usage
- Practical lab: Establishing observability dashboards for interactive agents
Enterprise Deployment Best Practices
- Striking a balance between interactivity, security, and governance
- Optimizing for performance and enhanced user experience
- Review of enterprise adoption case studies
Conclusion and Next Steps
Requirements
- Practical experience with Python or JavaScript for prototyping
- A solid understanding of designing LLM-powered applications
- Familiarity with cloud-based data workflows
Target Audience
- ML engineers
- Data scientists
- Developers with a focus on UX
