Gen AI Agentic Track
Completed by Marian Teodorescu on November 8, 2025
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AI Agentic Track
Learning objectives
- Design and implement agentic AI systems using LangChain and LangGraph, focusing on memory, iteration, and conditional logic.
- Develop AI agents with retrieval-augmented generation (RAG) and LangChain technologies, applying prompt engineering and in-context learning techniques.
- Build and manage agent memory systems using the Letta framework, enhancing LLM applications with persistent and self-editing memory capabilities.
- Create autonomous multi-agent systems using frameworks like LangGraph, CrewAI, BeeAI, and AG2, applying orchestration strategies and workflow patterns.
Skills learned
- Artificial Intelligence and Machine Learning (AI/ML)
- Generative AI Agents
- System Design and Implementation
- Large Language Modeling
- Natural Language Processing
- Real Time Data
- Responsible AI
- Artificial Intelligence
- Tool Calling
- LLM Application
- Software Design Patterns
- Generative AI
- Python Programming
- Agentic systems
- Data Validation
- Prompt Engineering
- Collaborative Software
- LangChain
- ChatGPT
- LangGraph
- OpenAI
- Data Science
- Application Design
- Software Development
- Skills section collapsed. Showing 6 of 24 skills.
Learning content completed
Some of the content and skills presented have been contributed by the content provider.