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Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore's 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: building a memory-enabled data analysis agent
Dynamic Computation with the Code Interpreter
- Supported operations and security constraints
- Executing transformations and calculations securely
- Practical lab: enabling real-time data transformations
Real-Time Interaction with the Browser Tool
- Setting up the browser tool for 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 tools
- 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: observability dashboards for interactive agents
Best Practices for Enterprise Deployment
- Balancing interactivity with security and governance
- Optimising for performance and user experience
- Enterprise adoption case studies
Summary and Next Steps
Requirements
- Experience with Python or JavaScript for prototyping purposes
- Understanding of application design powered by Large Language Models (LLMs)
- Familiarity with cloud-based data workflows
Audience
- Machine Learning engineers
- Data scientists
- Developers focused on User Experience (UX)
14 Hours
