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Duration 14 hours
Course Outline
Foundations of Interactive AI Agents
- Overview of AgentCore’s interactive functionalities
- Architecting complex workflows using memory and tools
- Applications across analytics, automation, and support sectors
Implementing AgentCore Memory
- Setting up session persistence mechanisms
- Developing multi-step workflows that maintain context awareness
- Practical lab: Creating a data analysis agent powered by memory
Achieving Dynamic Computation via Code Interpreter
- Reviewing supported operations and security boundaries
- Safely executing data transformations and calculations
- Practical lab: Facilitating real-time data processing
Enabling Real-Time Interaction with Browser Tool
- Configuring the browser tool for agent-centric workflows
- Handling data extraction and user interface manipulation
- Practical lab: Developing an agent capable of web interactions
Synthesizing Memory, Code, and Browser Capabilities
- Orchestrating workflows that span memory and external tools
- Designing multi-modal, highly interactive experiences
- Practical lab: Constructing a customer support assistant
Quality Assurance and Observability
- Troubleshooting interactive workflow issues
- Tracking and monitoring tool utilization
- Practical lab: Building observability dashboards for interactive agents
Enterprise Deployment Strategies
- Striking a balance between interactivity, security, and governance
- Enhancing performance and user experience metrics
- Examining real-world enterprise adoption scenarios
Wrap-up and Future Directions
Requirements
- Hands-on experience with Python or JavaScript for application prototyping
- Conceptual understanding of LLM-driven application architecture
- Aquaintance with cloud-based data processing workflows
Target Audience
- Machine Learning Engineers
- Data Scientists
- UX-oriented Developers
