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Duration 14 hours
Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore's interactive capabilities
- Designing rich workflows leveraging memory and tools
- Application scenarios in analytics, automation, and support
Working with AgentCore Memory
- Configuring session persistence
- Architecting multi-step, context-aware workflows
- Practical lab: developing a data analysis agent with memory features
Dynamic Computation with the Code Interpreter
- Supported operations and associated security constraints
- Safely executing transformations and calculations
- Practical lab: implementing real-time data transformations
Real-Time Interaction with the Browser Tool
- Configuring the browser tool for agent workflows
- Retrieving data and interacting with user interfaces
- Practical lab: creating an agent with web interaction capabilities
Combining Memory, Code, and Browser Tools
- Chaining workflows across memory and external tools
- Designing multi-modal, interactive workflows
- Practical lab: building a customer support assistant
Testing and Observability
- Debugging interactive workflow components
- Logging and monitoring tool usage
- Practical lab: implementing observability dashboards for interactive agents
Best Practices for Enterprise Deployment
- Balancing interactivity with security and governance requirements
- Optimizing for performance and user experience
- Analysis of enterprise adoption case studies
Summary and Next Steps
Requirements
- Practical experience with Python or JavaScript for prototyping purposes
- A solid understanding of LLM-driven application design
- Familiarity with cloud-based data workflows
Target Audience
- ML engineers
- Data scientists
- Developers with a UX focus
