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Duration 14 hours
Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore’s interactive capabilities
- Designing enriched workflows utilizing memory and tools
- Application scenarios across analytics, automation, and support
Utilizing AgentCore Memory
- Setting up session persistence
- Crafting multi-step, context-sensitive workflows
- Practical lab: developing a data analysis agent with memory capabilities
Dynamic Computation via the Code Interpreter
- Reviewing supported operations and security boundaries
- Safely executing transformations and calculations
- Practical lab: implementing real-time data transformations
Real-Time Interaction Using the Browser Tool
- Configuring the browser tool for agent workflows
- Managing data retrieval and user interface engagements
- Practical lab: creating an agent with web interaction features
Synergizing Memory, Code, and Browser Tools
- Linking workflows across memory and tool usage
- Designing multi-modal, interactive processes
- Practical lab: building a comprehensive customer support assistant
Testing and Observability
- Troubleshooting interactive workflow issues
- Tracking and monitoring tool utilization
- Practical lab: establishing observability dashboards for interactive agents
Best Practices for Enterprise Implementation
- Striking a balance between interactivity, security, and governance
- Enhancing performance and user experience
- Examining enterprise adoption case studies
Wrap-Up and Future Pathways
Requirements
- Proficiency in Python or JavaScript for prototyping
- Conceptual understanding of LLM-driven application design
- Knowledge of cloud-based data workflows
Target Audience
- ML engineers
- Data scientists
- UX-oriented developers
