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Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore's interactive features
- Designing comprehensive workflows utilizing memory and tools
- Applications in analytics, automation, and support domains
Utilizing AgentCore Memory
- Configuring session persistence mechanisms
- Crafting multi-step, context-sensitive workflows
- Lab exercise: constructing a data analysis agent with memory capabilities
Dynamic Computation via the Code Interpreter
- Reviewing supported operations and security limitations
- Safely executing data transformations and calculations
- Lab exercise: implementing real-time data transformations
Real-Time Interaction Using the Browser Tool
- Configuring the browser tool for agent workflows
- Handling data retrieval and user interface interactions
- Lab exercise: developing an agent with web interaction abilities
Synthesizing Memory, Code, and Browser Tools
- Sequencing workflows across memory and tool integrations
- Designing multi-modal, interactive user journeys
- Lab exercise: building a customer support assistant
Testing and Observability
- Troubleshooting interactive workflow components
- Tracking and monitoring tool utilization
- Lab exercise: implementing observability dashboards for interactive agents
Best Practices for Enterprise Deployment
- Balancing interactive features with security and governance standards
- Optimizing system performance and user experience
- Analysis of enterprise adoption case studies
Summary and Recommended Next Steps
Requirements
- Practical experience with Python or JavaScript for prototyping
- Conceptual understanding of LLM-powered application architecture
- Proficiency with cloud-based data workflows
Target Audience
- Machine Learning engineers
- Data scientists
- Developers with a focus on UX
14 Hours