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Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore’s interactive capabilities
- Architecting rich workflows utilizing memory and tools
- Application scenarios in analytics, automation, and support
Managing AgentCore Memory
- Configuring session persistence mechanisms
- Creating multi-step, context-aware process flows
- Practical lab: constructing a data analysis agent with memory retention
Dynamic Computation via Code Interpreter
- Reviewing supported operations and security boundaries
- Safely executing transformations and complex calculations
- Practical lab: implementing real-time data processing
Real-Time Engagement via Browser Tool
- Configuring the browser tool for agent-driven workflows
- Executing data retrieval and user interface interactions
- Practical lab: developing an agent with web interaction capabilities
Integrating Memory, Code, and Browser Tools
- Orchestrating workflows across memory and tool modules
- Designing multi-modal, interactive user journeys
- Practical lab: building an intelligent customer support assistant
Testing and Observability
- Debugging complex interactive workflows
- Logging and monitoring tool utilization metrics
- Practical lab: establishing observability dashboards for interactive agents
Best Practices for Enterprise Deployment
- Balancing high interactivity with security and governance standards
- Optimizing system performance and user experience
- Analysis of enterprise adoption case studies
Conclusion and Future Directions
Requirements
- Proficiency in Python or JavaScript for prototyping
- Foundational understanding of LLM-powered application architecture
- Experience with cloud-based data workflows
Target Audience
- ML engineers
- Data scientists
- UX-focused developers
14 Hours