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Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore’s interactive capabilities
- Designing complex workflows utilizing memory and tools
- Exploring use cases in analytics, automation, and support
Managing AgentCore Memory
- Setting up session persistence
- Architecting multi-step, context-aware workflows
- Practical lab: Creating a data analysis agent with memory capabilities
Dynamic Computation via Code Interpreter
- Reviewing supported operations and security restrictions
- Safely executing data transformations and calculations
- Practical lab: Implementing real-time data transformations
Real-Time Engagement with the Browser Tool
- Configuring the browser tool for agent workflows
- Managing data retrieval and user interface interactions
- Practical lab: Building an agent with web interaction abilities
Synthesizing Memory, Code, and Browser Tools
- Connecting workflows across memory and tool interfaces
- Designing multi-modal, interactive workflows
- Practical lab: Developing a customer support assistant
Testing and Observability
- Troubleshooting interactive workflows
- Tracking 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 performance and user experience
- Reviewing enterprise adoption case studies
Conclusion and Future Directions
Requirements
- Proficiency in Python or JavaScript for prototyping
- Comprehensive understanding of LLM-powered application architecture
- Working knowledge of cloud-based data workflows
Target Audience
- Machine Learning engineers
- Data scientists
- Developers with a focus on user experience
14 Hours