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Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore's interactive capabilities.
- Designing rich workflows using memory and tools.
- Exploration of use cases across analytics, automation, and support domains.
Working with AgentCore Memory
- Configuring session persistence.
- Designing multi-step workflows that retain context.
- Hands-on lab: Building a data analysis agent with memory capabilities.
Dynamic Computation with the Code Interpreter
- Supported operations and security constraints.
- Safely executing calculations and data transformations.
- Hands-on lab: Enabling real-time data transformations.
Real-Time Interaction with the Browser Tool
- Setting up the browser tool for agent workflows.
- Retrieving data and interacting with user interfaces.
- Hands-on lab: Building an agent with web interaction capabilities.
Combining Memory, Code, and Browser Tools
- Chaining workflows across memory storage and external tools.
- Designing multi-modal, interactive workflows.
- Hands-on lab: Building a customer support assistant.
Testing and Observability
- Debugging interactive workflows.
- Logging and monitoring tool usage.
- Hands-on lab: Creating observability dashboards for interactive agents.
Best Practices for Enterprise Deployment
- Balancing interactivity with security and governance requirements.
- Optimizing for performance and user experience.
- Reviewing enterprise adoption case studies.
Summary and Next Steps
Requirements
- Experience with Python or JavaScript for prototyping purposes.
- Understanding of application design driven by Large Language Models (LLMs).
- Familiarity with cloud-based data workflows.
Target Audience
- Machine Learning (ML) engineers.
- Data scientists.
- Developers focused on User Experience (UX).
14 Hours
