AI & Machine Learning
Agentic AI & AI Automation
Build AI agents with LangChain, LangGraph, RAG and MCP that use real tools.
Overview
Learn to build AI agents that reason, retrieve knowledge and take action. You connect large language models to your own data and tools with RAG, LangChain, LangGraph and the Model Context Protocol, and ship practical automation solutions.
Course objectives
- Design AI agents that plan and use tools
- Build agents with LangChain and LangGraph
- Ground answers in your data with RAG
- Connect models to tools with MCP
- Ship safe, practical AI automation
Who it's for
- Developers and DevOps engineers building AI-powered automation
- Engineers moving into AI engineering roles
- Technical leads exploring agentic AI for operations
Prerequisites
- Comfortable writing Python
- Basic understanding of APIs and JSON
- Generative AI basics are helpful
Syllabus outline
- What makes an AI system agentic
- Planning, memory and tool use
- When to use agents — and when not to
Lab and practice approach
Every module is hands-on: you build a RAG assistant, an MCP tool server and a working multi-step agent, ending with an automation capstone.
Why attend this course?
- Agentic AI is the fastest-growing area of AI engineering
- Directly applicable to IT operations and business automation
- Build working agents, not just demos
After completing this course
Roles you can aim for
- AI Engineer
- AI Automation Engineer
- Agentic AI Developer
What you will be able to do
- RAG systems
- LangGraph agent workflows
- MCP server development
Assessments and capstone project
No vendor exam is required. You are assessed through assignments, quizzes and knowledge checks, technical assessments and a final capstone project.
- Assessment
- Assignments, quizzes and technical assessments
- Final project
- Capstone project reviewed by your instructor
- Vendor exam
- Not required
Why learn with OpenSprig
- Live, instructor-led online sessions you can ask questions in
- Practice on real systems in every class
- Exam-objective-aligned syllabus with mock tests
- Course community group for help between classes
- Weekend-friendly 2–3 hour classes
Keep growing
Related courses
Generative AI & Prompt Engineering
Use ChatGPT, Gemini and Claude effectively and automate everyday work with AI.
- ChatGPT
- Gemini
- Claude
- Zapier / n8n
40–50 hours Hands-on labs
Python for AI & Machine Learning
Learn Python, NumPy and Pandas, and prepare data for machine learning.
- Python
- Jupyter
- NumPy
- Pandas
- Matplotlib
- Scikit-learn
40–50 hours Hands-on labs
Machine Learning Engineering
Train, evaluate and improve supervised and unsupervised ML models.
- Python
- Scikit-learn
- Pandas
- XGBoost
- Jupyter
40–50 hours Hands-on labs
Deep Learning & Neural Networks
Build neural networks for computer vision and NLP with TensorFlow and PyTorch.
- TensorFlow
- Keras
- PyTorch
- Hugging Face
- Google Colab
40–50 hours Hands-on labs