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Duration 14 hours
Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore's interactive features
- Architecting enriched workflows utilizing memory and tools
- Applications across analytics, automation, and support domains
Utilizing AgentCore Memory
- Establishing session persistence
- Creating multi-step, context-aware workflows
- Lab: Developing a data analysis agent with memory capabilities
Dynamic Computation via the Code Interpreter
- Reviewing supported operations and security protocols
- Safely executing transformations and calculations
- Lab: Implementing real-time data transformations
Real-Time Engagement Using the Browser Tool
- Configuring the browser tool for agent workflows
- Fetching data and interacting with user interfaces
- Lab: Creating an agent with web interaction skills
Integrating Memory, Code, and Browser Tools
- Orchestrating workflows across memory and tool components
- Designing multi-modal, interactive experiences
- Lab: Building a customer support assistant
Testing and Observability
- Troubleshooting interactive workflows
- Tracking and monitoring tool utilization
- Lab: Setting up observability dashboards for interactive agents
Best Practices for Enterprise Deployment
- Managing the balance between interactivity, security, and governance
- Optimizing performance and user experience
- Case studies on enterprise adoption
Conclusion and Future Steps
Requirements
- Proficiency in Python or JavaScript for rapid prototyping
- Knowledge of designing LLM-based applications
- Understanding of cloud-native data workflows
Target Audience
- Machine Learning Engineers
- Data Scientists
- Developers focused on user experience
