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Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore's interactive features
- Constructing rich workflows using memory and tools
- Application scenarios in analytics, automation, and support
Managing AgentCore Memory
- Setting up session persistence
- Creating multi-step, context-aware workflows
- Lab: Developing a memory-enabled data analysis agent
Dynamic Computation via Code Interpreter
- Supported operations and security limitations
- Safely executing transformations and calculations
- Lab: Enabling real-time data transformations
Real-Time Web Interaction with Browser Tool
- Configuring the browser tool for agent workflows
- Retrieving data and interacting with user interfaces
- Lab: Building an agent with web interaction capabilities
Synthesizing Memory, Code, and Browser Tools
- Connecting workflows across memory and tools
- Designing multi-modal, interactive experiences
- Lab: Developing a customer support assistant
Testing and Observability
- Debugging complex interactive workflows
- Logging and monitoring tool utilization
- Lab: Implementing observability dashboards for interactive agents
Enterprise Deployment Best Practices
- Balancing interactivity with security and governance
- Optimizing performance and user experience
- Review of enterprise adoption case studies
Conclusion and Future Directions
Requirements
- Proficiency in Python or JavaScript for prototyping
- Foundational understanding of LLM-driven application design
- Knowledge of cloud-based data workflows
Target Audience
- ML engineers
- Data scientists
- UX-focused developers
14 Hours
