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Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore’s interactive capabilities.
- Designing rich workflows using memory and tools.
- Use cases across analytics, automation, and support.
Working with AgentCore Memory
- Configuring session persistence.
- Designing multi-step, context-aware workflows.
- Hands-on lab: Building a memory-enabled data analysis agent.
Dynamic Computation with the Code Interpreter
- 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 for agent workflows.
- 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 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: Observability dashboards for interactive agents.
Best Practices for Enterprise Deployment
- Balancing interactivity with security and governance.
- Optimizing for performance and user experience.
- Enterprise adoption case studies.
Summary and Next Steps
Requirements
- Experience with Python or JavaScript for prototyping.
- Understanding of LLM-based application design.
- Familiarity with cloud-based data workflows.
Audience
- ML engineers.
- Data scientists.
- UX-focused developers.
14 Hours
