Gen AI Agentic Track
Completed by Indu Bousely on October 17, 2025
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AI Agentic Track
Learning objectives
- Design and implement agentic AI systems using LangChain, LangGraph, and RAG technologies to create self-improving and collaborative multi-agent systems.
- Develop AI agents with managed, persistent memory using frameworks like Letta and MemGPT, enabling advanced reasoning and memory management capabilities.
- Apply orchestration strategies and workflow patterns to build scalable and efficient AI applications using frameworks such as LangGraph, CrewAI, BeeAI, and AG2.
Skills learned
- Artificial Intelligence and Machine Learning (AI/ML)
- Generative AI Agents
- Large Language Modeling
- System Design and Implementation
- Natural Language Processing
- Real Time Data
- Software Design Patterns
- Agentic systems
- Data Validation
- Collaborative Software
- ChatGPT
- OpenAI
- Data Science
- Software Development
- Skills section collapsed. Showing 6 of 14 skills.
Learning content completed
Some of the content and skills presented have been contributed by the content provider.