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Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore’s interactive capabilities
- Designing robust workflows using memory and tools
- Application scenarios in analytics, automation, and support
Managing AgentCore Memory
- Configuring session persistence
- Crafting multi-step, context-aware workflows
- Hands-on lab: Developing a data analysis agent with memory capabilities
Dynamic Computation via the Code Interpreter
- Supported operations and security constraints
- Safely executing transformations and calculations
- Hands-on lab: Implementing real-time data transformations
Real-Time Interaction using the Browser Tool
- Setup of the browser tool for agent workflows
- Techniques for data retrieval and user interface interaction
- Hands-on lab: Creating an agent with web interaction features
Synthesizing Memory, Code, and Browser Tools
- Linking workflows across memory and various tools
- Designing multi-modal, interactive workflows
- Hands-on lab: Building a customer support assistant
Testing and Observability
- Debugging interactive workflows
- Logging and monitoring tool utilization
- Hands-on lab: Creating observability dashboards for interactive agents
Best Practices for Enterprise Deployment
- Balancing interactivity with security and governance
- Optimizing for performance and user experience
- Enterprise adoption case studies
Summary and Next Steps
Requirements
- Experience with Python or JavaScript for prototyping
- Understanding of LLM-powered application design
- Familiarity with cloud-based data workflows
Target Audience
- ML engineers
- Data scientists
- UX-focused developers
14 Hours