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Duration 14 hours
Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore's interactive capabilities
- Crafting rich workflows using memory and tools
- Applicable use cases in analytics, automation, and support
Working with AgentCore Memory
- Setting up session persistence
- Developing multi-step, context-aware workflows
- Practical lab: Creating a memory-enabled data analysis agent
Dynamic Computation via the Code Interpreter
- Review of supported operations and security limits
- Safe execution of transformations and calculations
- Practical lab: Enabling real-time data transformations
Real-Time Interaction using the Browser Tool
- Configuring the browser tool for agent workflows
- Performing data retrieval and user interface interactions
- Practical lab: Developing an agent with web interaction capabilities
Integrating Memory, Code, and Browser Tools
- Connecting workflows across memory and tools
- Designing multi-modal, interactive workflows
- Practical lab: Building a customer support assistant
Testing and Observability
- Troubleshooting interactive workflows
- Logging and monitoring tool utilization
- Practical lab: Implementing observability dashboards for interactive agents
Best Practices for Enterprise Deployment
- Striking a balance between interactivity, security, and governance
- Enhancing performance and user experience
- Case studies on enterprise adoption
Summary and Next Steps
Requirements
- Proficiency in Python or JavaScript for building prototypes
- Comprehension of application design powered by Large Language Models (LLMs)
- Working knowledge of cloud-based data processing workflows
Target Audience
- ML Engineers
- Data Scientists
- UX-Oriented Developers
