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Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore’s interactive capabilities
- Creating rich workflows leveraging memory and tools
- Application scenarios in analytics, automation, and support
Managing AgentCore Memory
- Configuring session persistence settings
- Architecting multi-step, context-aware processes
- Practical lab: Developing a memory-enabled data analysis agent
Dynamic Computation via Code Interpreter
- Review of supported operations and security protocols
- Safely executing data transformations and calculations
- Practical lab: Implementing real-time data processing capabilities
Real-Time Engagement with Browser Tool
- Integration of the browser tool into agent workflows
- Facilitating data retrieval and user interface interactions
- Practical lab: Constructing an agent with web interaction features
Synthesizing Memory, Code, and Browser Tools
- Orchestrating workflows across memory and auxiliary tools
- Designing multi-modal, interactive user experiences
- Practical lab: Building a comprehensive customer support assistant
Testing and Observability
- Troubleshooting and debugging interactive workflows
- Monitoring tool usage and logging activities
- Practical lab: Configuring observability dashboards for interactive agents
Best Practices for Enterprise Deployment
- Aligning interactivity with security and governance standards
- Optimizing for performance and user experience
- Analysis of enterprise adoption case studies
Conclusion and Future Directions
Requirements
- Proficiency in Python or JavaScript for prototyping
- Conceptual understanding of LLM-powered application design
- Knowledge of cloud-based data workflows
Target Audience
- Machine Learning Engineers
- Data Scientists
- UX-focused Developers
14 Hours
