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Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore interactive capabilities.
- Designing enriched workflows utilizing memory and tools.
- Exploring use cases in analytics, automation, and support.
Managing AgentCore Memory
- Configuring session persistence strategies.
- Architecting multi-step, context-aware workflows.
- Practical lab: Developing a memory-enabled data analysis agent.
Dynamic Computation via the Code Interpreter
- Reviewing supported operations and security constraints.
- Executing safe transformations and calculations.
- Practical lab: Implementing real-time data transformations.
Real-Time Interaction Using the Browser Tool
- Configuring the browser tool within agent workflows.
- Handling data retrieval and user interface interactions.
- Practical lab: Building an agent with web interaction capabilities.
Synergizing Memory, Code, and Browser Tools
- Chaining workflows across memory and tool integrations.
- Designing multi-modal, interactive user experiences.
- Practical lab: Constructing a customer support assistant.
Testing and Observability
- Debugging complex interactive workflows.
- Logging and monitoring tool utilization.
- Practical lab: Setting up observability dashboards for interactive agents.
Best Practices for Enterprise Deployment
- Balancing interactivity with security and governance requirements.
- Optimizing for performance and user experience.
- Reviewing enterprise adoption case studies.
Summary and Next Steps
Requirements
- Proficiency in Python or JavaScript for prototyping purposes.
- Comprehension of LLM-powered application design principles.
- Knowledge of cloud-based data workflows.
Target Audience
- ML Engineers
- Data Scientists
- UX-focused Developers
14 Hours
