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Course Outline
Foundations of Interactive AI Agents
- Overview of AgentCore’s interactive feature set
- Architecting sophisticated workflows using memory and external tools
- Exploring applications in analytics, automation, and support
Managing State with AgentCore Memory
- Setting up session persistence parameters
- Crafting multi-step, context-aware process flows
- Practical Lab: Developing a data analysis agent with memory capabilities
Dynamic Calculation via Code Interpreter
- Reviewing supported operations and security boundaries
- Executing safe data transformations and calculations
- Practical Lab: Implementing real-time data processing
Live Web Engagement with Browser Tools
- Configuring the browser tool within agent workflows
- Performing data retrieval and interface interactions
- Practical Lab: Building an agent with web browsing capabilities
Synthesizing Memory, Code, and Browser Capabilities
- Orchestrating workflows across memory and tool components
- Designing complex, multi-modal interactive experiences
- Practical Lab: Creating a comprehensive customer support assistant
Validation and Monitoring
- Debugging strategies for interactive processes
- Implementing logging and tool usage monitoring
- Practical Lab: Configuring observability dashboards for interactive agents
Enterprise Deployment Best Practices
- Striking a balance between interactivity, security, and governance
- Optimizing systems for performance and user satisfaction
- Reviewing enterprise adoption success stories
Conclusion and Future Directions
Requirements
- Proficiency in Python or JavaScript for application prototyping
- Foundational understanding of LLM-driven application architecture
- Working knowledge of cloud-based data processing workflows
Target Audience
- Machine Learning Engineers
- Data Scientists
- Developers with a focus on User Experience (UX)
14 Hours