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Duration 14 hours
Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore's interactive features
- Architecting rich workflows utilizing memory and tools
- Applications across analytics, automation, and support domains
Utilizing AgentCore Memory
- Configuring session persistence
- Designing multi-step, context-aware workflows
- Practical lab: Constructing a memory-driven data analysis agent
Dynamic Computation via Code Interpreter
- Supported operations and associated security constraints
- Safely executing transformations and calculations
- Practical lab: Implementing real-time data transformations
Real-Time Engagement with Browser Tool
- Configuring the browser tool for agent workflows
- Data retrieval and user interface interactions
- Practical lab: Developing an agent with web interaction capabilities
Integrating Memory, Code, and Browser Tools
- Chaining workflows across memory and utility tools
- Designing multi-modal, interactive workflows
- Practical lab: Building a comprehensive customer support assistant
Testing and Observability
- Debugging interactive workflow processes
- Logging and monitoring tool utilization
- Practical lab: Creating observability dashboards for interactive agents
Best Practices for Enterprise Deployment
- Balancing interactivity with security and governance standards
- Optimizing for performance and user experience
- Case studies on enterprise adoption strategies
Summary and Next Steps
Requirements
- Proficiency in Python or JavaScript for prototyping
- Understanding of application design powered by LLMs
- Familiarity with cloud-native data workflows
Target Audience
- ML engineers
- Data scientists
- Developers with a UX focus
