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Duration 14 hours
Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore’s interactive features
- Designing sophisticated workflows using memory and tools
- Real-world use cases in analytics, automation, and support
Utilizing AgentCore Memory
- Setting up session persistence
- Crafting multi-step, context-aware workflows
- Hands-on lab: Developing a memory-enabled data analysis agent
Dynamic Computation via the Code Interpreter
- Reviewing supported operations and security boundaries
- Safely executing transformations and calculations
- Hands-on lab: Enabling real-time data transformations
Real-Time Interaction via the Browser Tool
- Configuring the browser tool for agent workflows
- Managing data retrieval and user interface interactions
- Hands-on lab: Building an agent with web interaction capabilities
Integrating Memory, Code, and Browser Tools
- Linking workflows across memory and various tools
- Architecting multi-modal, interactive workflows
- Hands-on lab: Creating a customer support assistant
Testing and Observability
- Troubleshooting interactive workflows
- Implementing logging and monitoring for tool usage
- Hands-on lab: Setting up observability dashboards for interactive agents
Best Practices for Enterprise Deployment
- Balancing interactivity with security and governance standards
- Optimizing for high performance and superior user experience
- Reviewing enterprise adoption case studies
Summary and Next Steps
Requirements
- Proficiency in Python or JavaScript for prototyping
- Foundational understanding of LLM-driven application design
- Working knowledge of cloud-based data workflows
Target Audience
- ML Engineers
- Data Scientists
- UX-Oriented Developers
