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Course Outline
Introduction to Interactive AI Agents
- Overview of AgentCore’s interactive capabilities.
- Designing complex workflows utilizing memory and tools.
- Exploring use cases in analytics, automation, and support.
Working with AgentCore Memory
- Configuring session persistence for continuous context.
- Designing multi-step workflows that are context-aware.
- Hands-on lab: Developing a data analysis agent with memory capabilities.
Dynamic Computation with the Code Interpreter
- Reviewing supported operations and security constraints.
- Safely executing data transformations and calculations.
- Hands-on lab: Implementing real-time data transformations.
Real-Time Interaction with the Browser Tool
- Setting up the browser tool within agent workflows.
- Performing data retrieval and interacting with user interfaces.
- Hands-on lab: Building an agent capable of web interactions.
Integrating Memory, Code, and Browser Tools
- Chaining workflows across memory systems and tools.
- Designing multi-modal, interactive user experiences.
- Hands-on lab: Creating a comprehensive customer support assistant.
Testing and Observability
- Debugging complex interactive workflows.
- Logging and monitoring tool utilization metrics.
- Hands-on lab: Setting up observability dashboards for interactive agents.
Best Practices for Enterprise Deployment
- Balancing high interactivity with security and governance standards.
- Optimizing solutions for performance and user experience.
- Case studies on enterprise-level adoption.
Summary and Next Steps
Requirements
- Proficiency in Python or JavaScript for prototyping purposes.
- A solid understanding of application design powered by Large Language Models (LLMs).
- Familiarity with cloud-based data workflows and architecture.
Target Audience
- Machine Learning Engineers
- Data Scientists
- Developers with a focus on User Experience (UX)
14 Hours
