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Duration 14 hours
Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore's interactive capabilities
- Designing enriched workflows using memory and tools
- Applications across analytics, automation, and support domains
Working with AgentCore Memory
- Configuring session persistence mechanisms
- Structuring multi-step, context-aware workflows
- Practical lab: developing a memory-enabled data analysis agent
Dynamic Computation with the Code Interpreter
- Reviewing supported operations and security boundaries
- Executing safe transformations and calculations
- Practical lab: implementing real-time data transformations
Real-Time Interaction with the Browser Tool
- Configuring the browser tool for agent-driven workflows
- Managing data retrieval and user interface interactions
- Practical lab: constructing an agent with web interaction capabilities
Combining Memory, Code, and Browser Tools
- Orchestrating workflows across memory and external tools
- Architecting multi-modal, interactive user experiences
- Practical lab: building a comprehensive customer support assistant
Testing and Observability
- Debugging complex interactive workflows
- Implementing logging and monitoring for tool usage
- Practical lab: setting up observability dashboards for interactive agents
Best Practices for Enterprise Deployment
- Balancing interactivity with security and governance standards
- Optimizing performance and user experience metrics
- Examining enterprise adoption case studies
Summary and Next Steps
Requirements
- Proficiency in Python or JavaScript for prototyping purposes
- A solid understanding of LLM-driven application design
- Familiarity with cloud-based data workflows
Target Audience
- ML engineers
- Data scientists
- UX-focused developers
