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Duration 14 hours
Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore's interactive capabilities.
- Designing enriched workflows utilizing memory and tools.
- Exploring use cases in analytics, automation, and support.
Working with AgentCore Memory
- Configuring session persistence strategies.
- Architecting multi-step, context-aware workflows.
- Hands-on lab: Creating a data analysis agent with memory capabilities.
Dynamic Computation via the Code Interpreter
- Reviewing supported operations and security constraints.
- Safely executing transformations and calculations.
- Hands-on lab: Implementing real-time data transformations.
Real-Time Engagement with the Browser Tool
- Setting up the browser tool within agent workflows.
- Managing data retrieval and user interface interactions.
- Hands-on lab: Developing an agent with web interaction capabilities.
Integrating Memory, Code, and Browser Tools
- Chaining workflows across memory modules and tools.
- Designing multi-modal, interactive workflows.
- Hands-on lab: Building a comprehensive customer support assistant.
Testing and Observability
- Debugging complex interactive workflows.
- Logging and monitoring tool utilization.
- Hands-on lab: Establishing observability dashboards for interactive agents.
Best Practices for Enterprise Deployment
- Balancing interactivity with security and governance standards.
- Optimizing for performance and enhanced user experience.
- Reviewing enterprise adoption case studies.
Summary and Next Steps
Requirements
- Proficiency in Python or JavaScript for prototyping.
- A solid understanding of LLM-powered application design.
- Familiarity with cloud-based data workflows.
Target Audience
- Machine Learning Engineers.
- Data Scientists.
- UX-Focused Developers.
