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Duration 14 hours
Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore's interactive capabilities
- Designing rich workflows utilizing memory and tools
- Use cases spanning analytics, automation, and support domains
Working with AgentCore Memory
- Configuring session persistence
- Architecting multi-step, context-aware workflows
- Hands-on lab: Creating a memory-enabled data analysis agent
Dynamic Computation with the Code Interpreter
- Review of supported operations and security constraints
- Safe execution of transformations and calculations
- Hands-on lab: Enabling real-time data transformations
Real-Time Interaction with the Browser Tool
- Setting up the browser tool within agent workflows
- Managing data retrieval and user interface interactions
- Hands-on lab: Developing an agent with web interaction capabilities
Combining Memory, Code, and Browser Tools
- Chaining workflows across memory and tool integrations
- Designing multi-modal, interactive workflows
- Hands-on lab: Building a customer support assistant
Testing and Observability
- Debugging interactive workflows effectively
- Logging and monitoring tool usage patterns
- Hands-on lab: Creating observability dashboards for interactive agents
Best Practices for Enterprise Deployment
- Balancing interactivity with security and governance standards
- Optimizing for performance and user experience
- Examining enterprise adoption case studies
Summary and Next Steps
Requirements
- Proficiency in Python or JavaScript for prototyping purposes
- Comprehension of LLM-powered application design principles
- Knowledge of cloud-based data workflows
Target Audience
- ML engineers
- Data scientists
- UX-focused developers
