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Duration 14 hours
Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore’s interactive capabilities
- Designing complex workflows utilizing memory and tools
- Real-world use cases in analytics, automation, and support
Implementing AgentCore Memory
- Configuring session state persistence
- Architecting multi-step, context-aware processes
- Lab: Developing a memory-enhanced data analysis agent
Dynamic Computation via Code Interpreter
- Understanding supported operations and security boundaries
- Safely executing data transformations and calculations
- Lab: Enabling real-time data processing capabilities
Real-Time Engagement with Browser Tools
- Integrating browser tools into agent workflows
- Managing data retrieval and UI interactions
- Lab: Creating an agent with web interaction features
Synthesizing Memory, Code, and Browser Tools
- Orchestrating workflows across memory and external tools
- Designing multi-modal, interactive systems
- Lab: Building a comprehensive customer support assistant
Testing and Observability
- Debugging strategies for interactive workflows
- Logging and monitoring tool utilization
- Lab: Implementing observability dashboards for agents
Best Practices for Enterprise Deployment
- Balancing interactivity with security and governance standards
- Optimizing for performance and user experience
- Reviewing enterprise adoption case studies
Conclusion and Future Directions
Requirements
- Proficiency in Python or JavaScript for prototype development
- Solid understanding of LLM-driven application architecture
- Working knowledge of cloud-based data processing pipelines
Target Audience
- Machine Learning Engineers
- Data Scientists
- UX-Oriented Software Developers
