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Duration 14 hours
Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore interactive capabilities
- Designing rich workflows utilising memory and tools
- Use cases spanning analytics, automation, and support
Working with AgentCore Memory
- Configuring session persistence
- Designing multi-step, context-aware workflows
- Practical lab: developing a memory-enabled data analysis agent
Dynamic Computation via the Code Interpreter
- Review of supported operations and security constraints
- Safely executing transformations and calculations
- Practical lab: enabling real-time data transformations
Real-Time Interaction Using the Browser Tool
- Setting up the browser tool within agent workflows
- Data retrieval and user interface interactions
- Practical lab: building an agent with web interaction capabilities
Combining Memory, Code, and Browser Tools
- Chaining workflows across memory and tool components
- Designing multi-modal, interactive workflows
- Practical lab: building a customer support assistant
Testing and Observability
- Debugging interactive workflows
- Logging and monitoring tool usage
- Practical lab: setting up observability dashboards for interactive agents
Best Practices for Enterprise Deployment
- Balancing interactivity with security and governance requirements
- Optimising for performance and user experience
- Enterprise adoption case studies
Summary and Next Steps
Requirements
- Proficiency in Python or JavaScript for prototyping purposes
- A solid understanding of LLM-powered application design
- Familiarity with cloud-based data workflows
Target Audience
- ML engineers
- Data scientists
- UX-focused developers
