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Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore's interactive capabilities.
- Designing rich workflows utilizing memory and tools.
- Exploring use cases across analytics, automation, and support.
Working with AgentCore Memory
- Configuring session persistence.
- Designing multi-step, context-aware workflows.
- Hands-on lab: Developing a memory-enabled data analysis agent.
Dynamic Computation with the Code Interpreter
- Understanding supported operations and security constraints.
- Safely executing transformations and calculations.
- Hands-on lab: Enabling real-time data transformations.
Real-Time Interaction with the Browser Tool
- Setting up the browser tool within agent workflows.
- Performing data retrieval and user interface interactions.
- Hands-on lab: Building an agent with web interaction capabilities.
Combining Memory, Code, and Browser Tools
- Chaining workflows across memory and various tools.
- Designing multi-modal, interactive workflows.
- Hands-on lab: Building a customer support assistant.
Testing and Observability
- Debugging interactive workflows.
- Logging and monitoring tool usage.
- Hands-on lab: Creating observability dashboards for interactive agents.
Best Practices for Enterprise Deployment
- Balancing interactivity with security and governance requirements.
- Optimizing for performance and user experience.
- Reviewing enterprise adoption case studies.
Summary and Next Steps
Requirements
- Practical experience with Python or JavaScript for prototyping.
- Foundational understanding of LLM-powered application design.
- Familiarity with cloud-based data workflows.
Target Audience
- ML Engineers
- Data Scientists
- UX-focused Developers
14 Hours
