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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

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