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Duration 14 hours
Course Outline
Overview of Interactive AI Agents
- Review of AgentCore's interactive features
- Strategizing rich workflows using memory and tools
- Applications across analytics, automation, and support domains
Managing AgentCore Memory
- Setting up session persistence
- Creating multi-step, context-sensitive workflows
- Practical exercise: Developing a memory-enabled data analysis agent
Dynamic Processing via Code Interpreter
- Review of supported operations and security limitations
- Safe execution of data transformations and calculations
- Practical exercise: Enabling real-time data processing
Real-Time Engagement with Browser Tool
- Configuring the browser tool for agent-based workflows
- Retrieving data and interacting with user interfaces
- Practical exercise: Creating an agent with web interaction features
Integrating Memory, Code, and Browser Capabilities
- Linking workflows across memory modules and tools
- Designing multi-modal, interactive processes
- Practical exercise: Building a customer support assistant
Testing and Monitoring
- Debugging complex interactive workflows
- Tracking and monitoring tool utilization
- Practical exercise: Setting up observability dashboards for interactive agents
Best Practices for Enterprise Implementation
- Striking a balance between interactivity, security, and governance
- Optimizing for both performance and user experience
- Examining enterprise adoption examples
Conclusion and Future Steps
Requirements
- Proficiency in Python or JavaScript for prototyping applications
- Conceptual understanding of LLM-driven application design
- Acquaintance with cloud-based data processing workflows
Target Audience
- Machine Learning Engineers
- Data Scientists
- Developers with a focus on User Experience (UX)
