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Course Outline

Introduction to Interactive AI Agents

  • Overview of AgentCore's interactive features
  • Designing comprehensive workflows utilizing memory and tools
  • Applications in analytics, automation, and support domains

Utilizing AgentCore Memory

  • Configuring session persistence mechanisms
  • Crafting multi-step, context-sensitive workflows
  • Lab exercise: constructing a data analysis agent with memory capabilities

Dynamic Computation via the Code Interpreter

  • Reviewing supported operations and security limitations
  • Safely executing data transformations and calculations
  • Lab exercise: implementing real-time data transformations

Real-Time Interaction Using the Browser Tool

  • Configuring the browser tool for agent workflows
  • Handling data retrieval and user interface interactions
  • Lab exercise: developing an agent with web interaction abilities

Synthesizing Memory, Code, and Browser Tools

  • Sequencing workflows across memory and tool integrations
  • Designing multi-modal, interactive user journeys
  • Lab exercise: building a customer support assistant

Testing and Observability

  • Troubleshooting interactive workflow components
  • Tracking and monitoring tool utilization
  • Lab exercise: implementing observability dashboards for interactive agents

Best Practices for Enterprise Deployment

  • Balancing interactive features with security and governance standards
  • Optimizing system performance and user experience
  • Analysis of enterprise adoption case studies

Summary and Recommended Next Steps

Requirements

  • Practical experience with Python or JavaScript for prototyping
  • Conceptual understanding of LLM-powered application architecture
  • Proficiency with cloud-based data workflows

Target Audience

  • Machine Learning engineers
  • Data scientists
  • Developers with a focus on UX
 14 Hours

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