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Duration 14 hours
Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore's interactive capabilities
- Designing rich workflows using memory and tools
- Use cases spanning analytics, automation, and support
Working with AgentCore Memory
- Configuring session persistence
- Creating multi-step, context-aware workflows
- Practical lab: Developing a data analysis agent with memory
Dynamic Computation with the Code Interpreter
- Reviewing supported operations and security constraints
- Safely executing transformations and calculations
- Practical lab: Enabling real-time data transformations
Real-Time Interaction with the Browser Tool
- Configuring the browser tool for agent workflows
- Facilitating data retrieval and user interface interactions
- Practical lab: Building an agent with web interaction capabilities
Combining Memory, Code, and Browser Tools
- Chaining 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
Best Practices for Enterprise Deployment
- Balancing interactivity with security and governance
- Optimizing for performance and user experience
- Case studies on enterprise adoption
Summary and Next Steps
Requirements
- Proficiency in Python or JavaScript for prototyping
- Conceptual understanding of LLM-driven application design
- Working knowledge of cloud-based data workflows
Target Audience
- Machine Learning Engineers
- Data Scientists
- UX-Oriented Developers
