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Duration 14 hours
Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore's interactive capabilities
- Designing robust workflows using memory and tools
- Applications in analytics, automation, and support
Working with AgentCore Memory
- Configuring session persistence
- Crafting multi-step, context-aware workflows
- Practical lab: Creating a memory-enabled data analysis agent
Dynamic Computation with the Code Interpreter
- Supported operations and security guidelines
- Safely executing transformations and calculations
- Practical lab: Implementing real-time data transformations
Real-Time Interaction with the Browser Tool
- Setting up the browser tool for agent workflows
- Retrieving data and interacting with user interfaces
- Practical lab: Developing an agent with web interaction skills
Combining Memory, Code, and Browser Tools
- Chaining workflows across memory and tools
- Designing multi-modal, interactive experiences
- Practical lab: Building a comprehensive customer support assistant
Testing and Observability
- Debugging complex 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
- Optimizing performance and user experience
- Case studies on enterprise adoption
Summary and Next Steps
Requirements
- Proficiency in Python or JavaScript for prototyping.
- A solid understanding of application design powered by LLMs.
- Familiarity with cloud-based data workflows.
Target Audience
- ML Engineers
- Data Scientists
- UX-focused Developers
