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Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore's interactive capabilities
- Building robust workflows leveraging memory and tools
- Application scenarios in analytics, automation, and support
Managing AgentCore Memory
- Setting up session persistence
- Constructing multi-step, context-aware workflows
- Lab session: Creating a data analysis agent with memory features
Dynamic Computation via Code Interpreter
- Reviewing supported operations and security limitations
- Performing safe transformations and calculations
- Lab session: Implementing real-time data transformations
Real-Time Engagement with the Browser Tool
- Configuring the browser tool within agent workflows
- Executing data retrieval and UI interactions
- Lab session: Developing an agent with web interaction abilities
Integrating Memory, Code, and Browser Tools
- Orchestrating workflows that span memory and tools
- Designing multi-modal, interactive processes
- Lab session: Building a customer support assistant
Testing and Observability
- Troubleshooting interactive workflows
- Tracking and monitoring tool utilization
- Lab session: Setting up observability dashboards for interactive agents
Best Practices for Enterprise Rollout
- Balancing interactivity with security and governance controls
- Optimizing for performance and enhanced user experience
- Review of enterprise implementation case studies
Wrap-up and Recommended Next Steps
Requirements
- Proficiency in Python or JavaScript for building prototypes
- Foundational understanding of LLM-driven application design
- Familiarity with cloud-based data processing workflows
Target Audience
- ML Engineers
- Data Scientists
- Developers with a focus on UX
14 Hours
