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Course Outline

Introduction to Interactive AI Agents

  • Overview of AgentCore interactive capabilities
  • Designing enriched workflows utilizing memory and tools
  • Application scenarios across analytics, automation, and support

Working with AgentCore Memory

  • Configuring session persistence
  • Architecting multi-step, context-aware workflows
  • Practical lab: developing a memory-enabled data analysis agent

Dynamic Computation with the Code Interpreter

  • Supported operations and security constraints
  • Safely executing transformations and calculations
  • Practical lab: enabling real-time data transformations

Real-Time Interaction with the Browser Tool

  • Setting up the browser tool for agent workflows
  • Managing data retrieval and user interface interactions
  • Practical lab: building an agent with web interaction capabilities

Combining Memory, Code, and Browser Tools

  • Chaining workflows across memory and tools
  • Designing multi-modal, interactive workflows
  • Practical lab: developing a customer support assistant

Testing and Observability

  • Debugging interactive workflows
  • Logging and monitoring tool utilization
  • Practical lab: implementing observability dashboards for interactive agents

Best Practices for Enterprise Deployment

  • Balancing interactivity with security and governance
  • Optimizing performance and user experience
  • Enterprise adoption case studies

Summary and Next Steps

Requirements

  • Experience with Python or JavaScript for prototyping
  • Understanding of LLM-powered application design
  • Familiarity with cloud-based data workflows

Audience

  • ML engineers
  • Data scientists
  • UX-focused developers
 14 Hours

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