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Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore’s interactive features
- Architecting sophisticated workflows using memory and tools
- Applicability in analytics, automation, and support domains
Utilizing AgentCore Memory
- Configuring session state persistence
- Developing multi-step, context-sensitive workflows
- Practical lab: Constructing a data analysis agent with memory capabilities
Dynamic Computation via Code Interpreter
- Review of supported operations and security limitations
- Safe execution of transformations and calculations
- Practical lab: Implementing real-time data transformation processes
Real-Time Engagement with Browser Tools
- Integration of browser tools into agent workflows
- Methods for data retrieval and UI interaction
- Practical lab: Developing an agent equipped with web interaction skills
Synthesizing Memory, Code, and Browser Tools
- Orchestrating workflows that span memory and tool interactions
- Designing multi-modal, interactive processes
- Practical lab: Building a customer support assistant
Testing and Observability
- Techniques for debugging interactive workflows
- Strategies for logging and monitoring tool utilization
- Practical lab: Implementing observability dashboards for interactive agents
Best Practices for Enterprise Rollout
- Striking a balance between interactivity, security, and governance
- Optimization strategies for performance and user experience
- Review of enterprise adoption case studies
Conclusion and Recommendations for Next Steps
Requirements
- Proficiency in Python or JavaScript for application prototyping
- Knowledge of LLM-driven application architecture
- Familiarity with cloud-based data processing workflows
Target Audience
- Machine Learning engineers
- Data scientists
- Developers focused on User Experience (UX)
14 Hours
