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Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore interactive capabilities
- Designing sophisticated workflows utilizing memory and tools
- Application cases in analytics, automation, and support
Working with AgentCore Memory
- Configuring session persistence
- Architecting multi-step, context-aware workflows
- Practical lab: Developing a memory-enabled data analysis agent
Dynamic Computation with the Code Interpreter
- Reviewing supported operations and security constraints
- Executing safe transformations and calculations
- Practical lab: Enabling 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
- Practical lab: Constructing an agent with web interaction capabilities
Combining Memory, Code, and Browser Tools
- Orchestrating workflows across memory and various tools
- Designing multi-modal, interactive user experiences
- Practical lab: Building a customer support assistant
Testing and Observability
- Debugging complex interactive workflows
- Implementing logging and monitoring for tool usage
- Practical lab: Setting up observability dashboards for interactive agents
Best Practices for Enterprise Deployment
- Balancing interactivity with security and governance protocols
- Optimizing for performance and user experience
- Enterprise adoption case studies
Summary and Next Steps
Requirements
- Proficiency in Python or JavaScript for prototyping
- Fundamental understanding of LLM-powered application architecture
- Familiarity with cloud-based data workflows
Target Audience
- ML engineers
- Data scientists
- UX-focused developers
14 Hours
