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Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore's interactive capabilities.
- Designing sophisticated workflows utilizing memory and tools.
- Exploring use cases across analytics, automation, and support domains.
Managing AgentCore Memory
- Setting up session persistence.
- Crafting multi-step, context-aware processes.
- Hands-on lab: Creating a data analysis agent with memory capabilities.
Dynamic Computation via Code Interpreter
- Reviewing supported operations and security boundaries.
- Safely executing transformations and calculations.
- Hands-on lab: Implementing real-time data transformations.
Real-Time Interaction Using the Browser Tool
- Configuring the browser tool within agent workflows.
- Executing data retrieval and UI interactions.
- Hands-on lab: Developing an agent with web interaction abilities.
Synthesizing Memory, Code, and Browser Tools
- Connecting workflows across memory and various tools.
- Designing multi-modal, interactive experiences.
- Hands-on lab: Building a comprehensive customer support assistant.
Testing and Observability
- Debugging complex interactive workflows.
- Tracking and monitoring tool utilization.
- Hands-on lab: Creating observability dashboards for interactive agents.
Best Practices for Enterprise Rollout
- Striking a balance between interactivity, security, and governance.
- Optimizing system performance and user experience.
- Reviewing enterprise adoption case studies.
Summary and Path Forward
Requirements
- Practical experience with Python or JavaScript for prototyping.
- A solid understanding of LLM-driven application design.
- Familiarity with cloud-based data workflows.
Target Audience
- ML Engineers
- Data Scientists
- UX-Focused Developers
14 Hours
