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Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore’s interactive capabilities
- Architecting rich workflows with memory and tools
- Applications across analytics, automation, and support
Leveraging 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
- Supported operations and security boundaries
- Safely executing 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: Building an agent with web interaction capabilities
Integrating Memory, Code, and Browser Tools
- Orchestrating workflows across memory and tools
- Designing multi-modal, interactive experiences
- Practical lab: Building a customer support assistant
Testing and Observability
- Debugging complex interactive workflows
- Logging and monitoring tool utilisation
- Practical lab: Setting up observability dashboards for interactive agents
Enterprise Deployment Best Practices
- Balancing interactivity with security and governance
- Optimising for performance and user experience
- Review of enterprise adoption case studies
Summary and Next Steps
Requirements
- Proficiency in Python or JavaScript for prototyping
- Understanding of LLM-driven application design
- Experience with cloud-based data workflows
Target Audience
- ML engineers
- Data scientists
- UX-focused developers
14 Hours
