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Duration 14 hours
Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore’s interactive capabilities
- Designing rich workflows utilizing memory and tools
- Key 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 via the Code Interpreter
- Reviewing supported operations and security constraints
- Executing safe transformations and calculations
- Hands-on lab: Implementing real-time data transformations
Real-Time Interaction Using the Browser Tool
- Setting up the browser tool for agent workflows
- Performing data retrieval and UI interactions
- Hands-on lab: Building an agent with web interaction capabilities
Synergizing Memory, Code, and Browser Tools
- Chaining workflows across memory and tool integrations
- Designing multi-modal, interactive workflows
- Hands-on lab: Constructing a customer support assistant
Testing and Observability
- Debugging complex interactive workflows
- Logging and monitoring tool usage
- Hands-on lab: Implementing observability dashboards for interactive agents
Best Practices for Enterprise Deployment
- Balancing interactivity with security and governance
- Optimizing for performance and user experience
- Insights from enterprise adoption case studies
Summary and Next Steps
Requirements
- Practical experience with Python or JavaScript for prototyping
- A solid grasp of LLM-driven application design
- Familiarity with cloud-based data workflows
Target Audience
- ML Engineers
- Data Scientists
- UX-focused Developers
