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Course Outline

Foundations of Interactive AI Agents

  • Overview of AgentCore’s interactive feature set
  • Architecting sophisticated workflows using memory and external tools
  • Exploring applications in analytics, automation, and support

Managing State with AgentCore Memory

  • Setting up session persistence parameters
  • Crafting multi-step, context-aware process flows
  • Practical Lab: Developing a data analysis agent with memory capabilities

Dynamic Calculation via Code Interpreter

  • Reviewing supported operations and security boundaries
  • Executing safe data transformations and calculations
  • Practical Lab: Implementing real-time data processing

Live Web Engagement with Browser Tools

  • Configuring the browser tool within agent workflows
  • Performing data retrieval and interface interactions
  • Practical Lab: Building an agent with web browsing capabilities

Synthesizing Memory, Code, and Browser Capabilities

  • Orchestrating workflows across memory and tool components
  • Designing complex, multi-modal interactive experiences
  • Practical Lab: Creating a comprehensive customer support assistant

Validation and Monitoring

  • Debugging strategies for interactive processes
  • Implementing logging and tool usage monitoring
  • Practical Lab: Configuring observability dashboards for interactive agents

Enterprise Deployment Best Practices

  • Striking a balance between interactivity, security, and governance
  • Optimizing systems for performance and user satisfaction
  • Reviewing enterprise adoption success stories

Conclusion and Future Directions

Requirements

  • Proficiency in Python or JavaScript for application prototyping
  • Foundational understanding of LLM-driven application architecture
  • Working knowledge of cloud-based data processing workflows

Target Audience

  • Machine Learning Engineers
  • Data Scientists
  • Developers with a focus on User Experience (UX)
 14 Hours

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