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Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore's interactive capabilities
- Crafting rich workflows using memory and tools
- Exploring use cases in analytics, automation, and support
Working with AgentCore Memory
- Configuring session persistence
- Designing multi-step, context-aware workflows
- Lab exercise: Building a memory-enabled data analysis agent
Dynamic Computation with the Code Interpreter
- Reviewing supported operations and security constraints
- Executing safe transformations and calculations
- Lab exercise: Implementing real-time data transformations
Real-Time Interaction with the Browser Tool
- Configuring the browser tool for agent workflows
- Managing data retrieval and user interface interactions
- Lab exercise: Developing an agent with web interaction capabilities
Combining Memory, Code, and Browser Tools
- Orchestrating workflows across memory and tools
- Designing multi-modal, interactive experiences
- Lab exercise: Creating a customer support assistant
Testing and Observability
- Debugging interactive workflows
- Implementing logging and monitoring for tool usage
- Lab exercise: Setting up observability dashboards for interactive agents
Best Practices for Enterprise Deployment
- Balancing interactivity with security and governance standards
- Optimizing for performance and user experience
- Reviewing enterprise adoption case studies
Summary and Next Steps
Requirements
- Proficiency in Python or JavaScript for prototyping
- A solid understanding of LLM-driven application design
- Experience with cloud-based data workflows
Target Audience
- ML engineers
- Data scientists
- Developers with a UX focus
14 Hours
