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Duration 14 hours
Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore's interactive capabilities
- Strategies for designing rich workflows using memory and tools
- Exploring use cases in analytics, automation, and support
Working with AgentCore Memory
- Configuring session persistence settings
- Crafting multi-step, context-aware workflows
- Practical lab: Building a data analysis agent with memory capabilities
Dynamic Computation with the Code Interpreter
- Reviewing supported operations and security constraints
- Safely executing transformations and calculations
- Practical lab: Enabling real-time data transformations
Real-Time Interaction with the Browser Tool
- Setting up the browser tool within agent workflows
- Managing data retrieval and user interface interactions
- Practical lab: Building an agent with web interaction features
Combining Memory, Code, and Browser Tools
- Chaining workflows across memory and various tools
- Designing multi-modal, interactive experiences
- Practical lab: Building a customer support assistant
Testing and Observability
- Debugging interactive workflow processes
- Logging and monitoring tool usage patterns
- Practical lab: Creating observability dashboards for interactive agents
Best Practices for Enterprise Deployment
- Balancing interactivity with security and governance requirements
- Optimizing for both performance and user experience
- Reviewing enterprise adoption case studies
Summary and Next Steps
Requirements
- Proficiency in Python or JavaScript for prototyping purposes
- A solid understanding of LLM-driven application architecture
- Knowledge of cloud-based data processing workflows
Target Audience
- Machine Learning engineers
- Data scientists
- Developers with a focus on User Experience (UX)
