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Duration 14 hours
Course Outline
Overview of Interactive AI Agents
- Surveying AgentCore's interactive capabilities
- Architecting rich workflows using memory and tools
- Applications in analytics, automation, and support
Managing AgentCore Memory
- Setting up session persistence
- Creating multi-step workflows with context awareness
- Practical lab: Developing a data analysis agent with memory support
Enabling Dynamic Computation via Code Interpreter
- Reviewing supported operations and security limits
- Performing safe calculations and transformations
- Practical lab: Executing real-time data transformations
Facilitating Real-Time Interaction with Browser Tool
- Configuring the browser tool for agent integration
- Handling data fetching and interface interactions
- Practical lab: Creating an agent with web interaction features
Integrating Memory, Code, and Browser Tools
- Linking workflows across memory and tools
- Designing multi-modal, interactive processes
- Practical lab: Building a customer support assistant
Testing and Monitoring
- Troubleshooting interactive workflows
- Tracking tool usage through logs and monitoring
- Practical lab: Implementing observability dashboards for interactive agents
Enterprise Deployment Best Practices
- Striking a balance between interactivity, security, and governance
- Enhancing performance and user experience
- Reviewing enterprise adoption examples
Conclusion and Future Directions
Requirements
- Proficiency in Python or JavaScript for building prototypes
- Conceptual understanding of LLM-driven application architecture
- Knowledge of cloud-native data workflows
Target Audience
- Machine Learning Engineers
- Data Scientists
- Developers with a focus on User Experience (UX)
