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Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore’s interactive capabilities
- Designing enriched workflows using memory and tools
- Applications across analytics, automation, and support domains
Utilizing AgentCore Memory
- Configuring session persistence mechanisms
- Architecting multi-step, context-aware workflows
- Practical Lab: Developing a data analysis agent with memory capabilities
Dynamic Computation via Code Interpreter
- Reviewing supported operations and security boundaries
- Safely executing transformations and complex calculations
- Practical Lab: Implementing real-time data transformation pipelines
Real-Time Web Interaction with Browser Tool
- Configuring the browser tool for agent-based workflows
- Techniques for data retrieval and UI engagement
- Practical Lab: Constructing an agent with web interaction features
Integrating Memory, Code, and Browser Tools
- Chaining workflows across memory systems and external tools
- Designing multi-modal, interactive user experiences
- Practical Lab: Building a comprehensive customer support assistant
Testing and Observability
- Debugging strategies for complex interactive workflows
- Logging and monitoring tool utilization
- Practical Lab: Creating observability dashboards for interactive agents
Best Practices for Enterprise Rollout
- Balancing user interactivity with security and governance standards
- Optimizing for system performance and user experience
- Examining enterprise adoption case studies
Wrap-Up and Future Directions
Requirements
- Practical experience with Python or JavaScript for prototyping
- A solid understanding of LLM-driven application design
- Familiarity with cloud-based data processing workflows
Target Audience
- Machine Learning Engineers
- Data Scientists
- UX-Focused Developers
14 Hours
