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Course Outline
Fundamentals of Interactive AI Agents
- Overview of AgentCore’s interactive features
- Architecting complex workflows using memory and tools
- Application scenarios in analytics, automation, and support
Managing State with AgentCore Memory
- Setting up persistent session state
- Creating multi-step workflows that retain context
- Practical lab: Developing a data analysis agent with memory capabilities
Performing Dynamic Calculations via Code Interpreter
- Reviewing supported operations and security limitations
- Safely executing data transformations and computations
- Practical lab: Implementing real-time data processing
Engaging with Web Content Using the Browser Tool
- Configuring the browser tool for agent tasks
- Retrieving data and interacting with user interfaces
- Practical lab: Creating an agent capable of web-based interactions
Integrating Memory, Code, and Browser Capabilities
- Linking workflows across memory and tool integrations
- Designing multi-modal, interactive user journeys
- Practical lab: Building a comprehensive customer support assistant
Validation and Monitoring
- Debugging complex interactive workflows
- Tracking and monitoring tool usage logs
- Practical lab: Setting up observability dashboards for agents
Strategies for Enterprise Rollout
- Balancing user interactivity with security and governance standards
- Enhancing performance and user experience
- Analysis of enterprise adoption case studies
Recap and Future Pathways
Requirements
- Proficiency in Python or JavaScript for prototyping
- Conceptual understanding of LLM-powered application architecture
- Working knowledge of cloud-based data pipelines
Target Audience
- Machine Learning Engineers
- Data Scientists
- UX-focused Developers
14 Hours
