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Duration 14 hours
Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore's interactive capabilities
- Designing complex workflows using memory and tools
- Application cases across analytics, automation, and support
Working with AgentCore Memory
- Configuring session persistence
- Designing multi-step, context-aware workflows
- Practical exercise: Developing a memory-enabled data analysis agent
Dynamic Computation with the Code Interpreter
- Supported operations and security limitations
- Safely executing transformations and calculations
- Practical exercise: Facilitating 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 exercise: Constructing an agent with web interaction capabilities
Combining Memory, Code, and Browser Tools
- Chaining workflows across memory and tools
- Architecting multi-modal, interactive workflows
- Practical exercise: Developing a customer support assistant
Testing and Observability
- Debugging interactive workflows
- Logging and monitoring tool usage
- Practical exercise: 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
- Proficiency in Python or JavaScript for prototyping
- Understanding of LLM-driven application architecture
- Familiarity with cloud-based data workflows
Target Audience
- ML engineers
- Data scientists
- UX-focused developers
