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Duration 14 hours
Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore’s interactive capabilities
- Designing robust workflows using memory and tools
- Applicable use cases in analytics, automation, and support
Utilizing AgentCore Memory
- Setting up session persistence
- Creating multi-step, context-aware workflows
- Practical lab: Developing a memory-enabled data analysis agent
Dynamic Computation via Code Interpreter
- Review of supported operations and security limitations
- Safely executing transformations and calculations
- Practical lab: Implementing real-time data transformations
Real-Time Interaction through Browser Tool
- Configuring the browser tool for agent workflows
- Managing data retrieval and user interface interactions
- Practical lab: Creating an agent with web interaction capabilities
Integrating Memory, Code, and Browser Tools
- Chaining workflows across memory and external tools
- Designing multi-modal, interactive workflows
- Practical lab: Building a comprehensive customer support assistant
Testing and Observability
- Debugging complex interactive workflows
- Logging and monitoring tool utilization
- Practical lab: Setting up observability dashboards for interactive agents
Best Practices for Enterprise Deployment
- Striking a balance between interactivity, security, and governance
- Optimizing for performance and enhanced user experience
- Insights from enterprise adoption case studies
Summary and Next Steps
Requirements
- Proficiency in Python or JavaScript for prototyping purposes
- A solid understanding of designing LLM-powered applications
- Experience with cloud-based data workflows
Target Audience
- Machine Learning Engineers
- Data Scientists
- UX-focused Developers
