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Course Outline
Foundations of Interactive AI Agents
- Overview of AgentCore’s interactive feature set
- Architecting rich workflows using memory and auxiliary tools
- Application scenarios in analytics, automation, and support
Implementing AgentCore Memory
- Setting up persistent session states
- Crafting multi-step, context-sensitive processes
- Practical lab: developing a data analysis agent with memory capabilities
Dynamic Processing via the Code Interpreter
- Reviewing supported operations and security boundaries
- Safely executing complex transformations and calculations
- Practical lab: facilitating real-time data transformation pipelines
Live Web Interaction via the Browser Tool
- Configuring the browser tool for agent-centric tasks
- Performing data retrieval and UI-level interactions
- Practical lab: creating an agent with native web interaction skills
Integrating Memory, Code, and Browser Capabilities
- Sequencing workflows across memory modules and external tools
- Designing multi-modal, highly interactive processes
- Practical lab: building a comprehensive customer support assistant
Quality Assurance and Observability
- Troubleshooting interactive workflow issues
- Logging activities and monitoring tool utilization
- Practical lab: deploying observability dashboards for interactive agents
Enterprise Deployment Best Practices
- Harmonizing interactivity with security protocols and governance
- Refining performance and user experience metrics
- Insights from enterprise adoption case studies
Conclusions and Future Directions
Requirements
- Practical experience in Python or JavaScript for rapid prototyping
- Conceptual understanding of LLM-driven application architecture
- Working knowledge of cloud-native data pipelines
Target Audience
- Machine Learning Engineers
- Data Scientists
- Developers focused on User Experience
14 Hours
