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Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore's interactive features
- Architecting rich workflows using memory and tools
- Applications across analytics, automation, and support
Utilizing AgentCore Memory
- Configuring session persistence
- Designing multi-step, context-sensitive workflows
- Lab exercise: Creating a data analysis agent with memory capabilities
Dynamic Computation via Code Interpreter
- Supported operations and security boundaries
- Safely executing transformations and calculations
- Lab exercise: Implementing real-time data transformations
Real-Time Interaction Using the Browser Tool
- Integrating the browser tool into agent workflows
- Data acquisition and user interface interactions
- Lab exercise: Developing an agent with web interaction features
Synergizing Memory, Code, and Browser Tools
- Sequencing workflows across memory and tools
- Designing multi-modal, interactive experiences
- Lab exercise: Building a comprehensive customer support assistant
Testing and Observability
- Troubleshooting interactive workflows
- Logging and monitoring tool utilization
- Lab exercise: Setting up observability dashboards for interactive agents
Enterprise Deployment Best Practices
- Balancing interactivity with security and governance
- Optimizing for performance and user experience
- Enterprise adoption case studies
Conclusion and Future Directions
Requirements
- Proficiency in Python or JavaScript for prototyping
- A solid grasp of application design powered by LLMs
- Working knowledge of cloud-based data workflows
Target Audience
- ML engineers
- Data scientists
- UX-centric developers
14 Hours
