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Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore's interactive capabilities
- Designing complex workflows leveraging memory and tools
- Applications across analytics, automation, and support domains
Working with AgentCore Memory
- Setting up session persistence
- Creating multi-step, context-aware processes
- Practical lab: Developing a data analysis agent with memory features
Dynamic Computation with the Code Interpreter
- Review of supported operations and security guidelines
- Executing safe transformations and calculations
- Practical lab: Enabling real-time data processing
Real-Time Interaction with the Browser Tool
- Configuring the browser tool for agent integration
- Managing data retrieval and user interface engagements
- Practical lab: Creating an agent with web interaction skills
Combining Memory, Code, and Browser Tools
- Orchestrating workflows across memory and various tools
- Designing multi-modal, interactive experiences
- Practical lab: Building a customer support assistant
Testing and Observability
- Debugging interactive workflows
- Tracking and monitoring tool usage
- Practical lab: Setting up observability dashboards for interactive agents
Best Practices for Enterprise Deployment
- Striking a balance between interactivity, 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
- Working understanding of LLM-powered application design
- Knowledge of cloud-based data workflows
Target Audience
- ML Engineers
- Data Scientists
- UX-Focused Developers
14 Hours
