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Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore interactive capabilities
- Designing enriched workflows utilizing memory and tools
- Application scenarios across analytics, automation, and support
Utilizing AgentCore Memory
- Configuring session persistence
- Architecting multi-step, context-aware workflows
- Practical lab: creating a data analysis agent with memory capabilities
Dynamic Computation via the Code Interpreter
- Supported operations and security limitations
- Safely executing transformations and calculations
- Practical lab: implementing real-time data transformations
Real-Time Engagement with the Browser Tool
- Configuring the browser tool for agent workflows
- Data retrieval and user interface engagement
- Practical lab: developing an agent with web interaction skills
Integrating Memory, Code, and Browser Tools
- Connecting workflows across memory and tools
- Designing multi-modal, interactive workflows
- Practical lab: constructing a customer support assistant
Testing and Observability
- Debugging interactive workflows
- Logging and monitoring tool utilization
- Practical lab: setting up observability dashboards for interactive agents
Enterprise Deployment Best Practices
- Aligning interactivity with security and governance
- Optimizing for performance and user experience
- Enterprise adoption case studies
Summary and Next Steps
Requirements
- Proficiency in Python or JavaScript for prototyping
- Comprehension of LLM-driven application architecture
- Knowledge of cloud-based data workflows
Audience
- ML engineers
- Data scientists
- UX-focused developers
14 Hours