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Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore's interactive features
- Building rich workflows using memory and tools
- Applications in analytics, automation, and support
Managing AgentCore Memory
- Configuring session persistence
- Creating multi-step, context-aware workflows
- Practical lab: Developing a memory-enabled data analysis agent
Dynamic Computation via Code Interpreter
- Understanding supported operations and security limits
- Safely executing transformations and calculations
- Practical lab: Facilitating real-time data transformations
Real-Time Interaction Using the Browser Tool
- Setting up the browser tool for agent workflows
- Performing data retrieval and UI interactions
- Practical lab: Building an agent with web interaction capabilities
Integrating Memory, Code, and Browser Tools
- Chaining workflows across memory and various tools
- Designing multi-modal, interactive workflows
- Practical lab: Creating a customer support assistant
Testing and Observability
- Debugging interactive workflows
- Logging and monitoring tool usage
- Practical lab: Implementing observability dashboards for interactive agents
Best Practices for Enterprise Deployment
- Aligning interactivity with security and governance standards
- Optimizing for performance and user experience
- Reviewing enterprise adoption case studies
Summary and Next Steps
Requirements
- Proficiency in Python or JavaScript for prototyping
- Conceptual understanding of LLM-driven application design
- Experience with cloud-based data workflows
Target Audience
- ML engineers
- Data scientists
- UX-oriented developers
14 Hours
