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Course Outline
Foundations of Interactive AI Agents
- Overview of AgentCore’s interactive capabilities
- Architecting rich workflows using memory and tools
- Application scenarios in analytics, automation, and support
Leveraging AgentCore Memory
- Setting up session persistence
- Creating multi-step, context-aware workflows
- Practical lab: Developing a memory-enabled data analysis agent
Dynamic Computation via the Code Interpreter
- Supported operations and security guidelines
- Safely executing transformations and calculations
- Practical lab: Implementing real-time data transformations
Real-Time Interaction Using the Browser Tool
- Configuring the browser tool for agent workflows
- Retrieving data and interacting with user interfaces
- Practical lab: Building an agent with web interaction capabilities
Integrating Memory, Code, and Browser Tools
- Chaining workflows across memory and various tools
- Designing multi-modal, interactive experiences
- Practical lab: Developing a comprehensive customer support assistant
Testing and Observability
- Debugging complex interactive workflows
- Logging and monitoring tool utilization
- Practical lab: Setting up observability dashboards for interactive agents
Best Practices for Enterprise Deployment
- Balancing interactivity with security and governance standards
- Optimizing for performance and user experience
- Examining enterprise adoption case studies
Summary and Future Steps
Requirements
- Proficiency in Python or JavaScript for prototyping
- Solid understanding of LLM-driven application design
- Familiarity with cloud-based data workflows
Target Audience
- ML engineers
- Data scientists
- UX-focused developers
14 Hours
