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Duration 14 hours
Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore's interactive capabilities
- Creating rich workflows using memory and tools
- Applications in analytics, automation, and support
Utilizing AgentCore Memory
- Setting up session persistence
- Developing multi-step, context-aware workflows
- Hands-on lab: Creating a data analysis agent with memory capabilities
Dynamic Computation via the Code Interpreter
- Reviewing supported operations and security limits
- Safely executing transformations and calculations
- Hands-on lab: Enabling real-time data processing
Real-Time Interaction Using the Browser Tool
- Configuring the browser tool for agent tasks
- Retrieving data and engaging with user interfaces
- Hands-on lab: Developing an agent with web interaction skills
Integrating Memory, Code, and Browser Tools
- Linking workflows across memory and tool usage
- Creating multi-modal, interactive experiences
- Hands-on lab: Building a customer support assistant
Testing and Observability
- Troubleshooting interactive workflows
- Tracking and monitoring tool utilization
- Hands-on lab: Implementing observability dashboards for agents
Best Practices for Enterprise Deployment
- Striking a balance between interactivity, security, and governance
- Enhancing performance and user experience
- Examining enterprise adoption case studies
Conclusion and Future Steps
Requirements
- Proficiency in Python or JavaScript for prototyping
- Familiarity with LLM-driven application architecture
- Knowledge of cloud-based data workflows
Target Audience
- ML Engineers
- Data Scientists
- UX-Oriented Developers
