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Course Outline

Introduction to Interactive AI Agents

  • Overview of AgentCore’s interactive capabilities
  • Architecting rich workflows utilizing memory and tools
  • Application scenarios in analytics, automation, and support

Managing AgentCore Memory

  • Configuring session persistence mechanisms
  • Creating multi-step, context-aware process flows
  • Practical lab: constructing a data analysis agent with memory retention

Dynamic Computation via Code Interpreter

  • Reviewing supported operations and security boundaries
  • Safely executing transformations and complex calculations
  • Practical lab: implementing real-time data processing

Real-Time Engagement via Browser Tool

  • Configuring the browser tool for agent-driven workflows
  • Executing data retrieval and user interface interactions
  • Practical lab: developing an agent with web interaction capabilities

Integrating Memory, Code, and Browser Tools

  • Orchestrating workflows across memory and tool modules
  • Designing multi-modal, interactive user journeys
  • Practical lab: building an intelligent customer support assistant

Testing and Observability

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

Best Practices for Enterprise Deployment

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

Conclusion and Future Directions

Requirements

  • Proficiency in Python or JavaScript for prototyping
  • Foundational understanding of LLM-powered application architecture
  • Experience with cloud-based data workflows

Target Audience

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

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