Udemy – Agentic AI From Foundations to Enterprise-Grade Systems

Welcome to Agentic AI: From Foundations to Enterprise-Grade Systems — your complete hands-on guide to designing, building, and deploying intelligent AI agents for real-world applications.

This course is built for developers, AI enthusiasts, and enterprise architects who want to go beyond prompting and explore the agentic capabilities of modern LLMs (Large Language Models). You’ll learn how to structure AI agents, empower them with tools, manage their memory and state, and evolve them into enterprise-grade, multi-agent systems.

What You Will Learn:

• The fundamentals of Agentic AI and how it differs from traditional prompt engineering
• Core architectural patterns like the ReAct pattern (Reasoning + Acting)
• How to build a minimal ReAct agent from scratch in Python
• How to integrate tools like web search, calculators, databases, APIs, and custom functions
• Implementing multi-turn reasoning and agent tool-chaining
• Handling errors, timeouts, and tool failures gracefully
• Adding logging, monitoring, and agent evaluation capabilities
• Architecting hierarchical agents, multi-agent collaborations, and role-based delegation
• Designing and deploying enterprise-grade agents with:
  • LangChain
  • LangGraph
  • CrewAI
  • FAISS Vector Stores
  • OpenAI & Hugging Face Models
  • FastAPI / Flask
  • Cloud / On-Prem Deployment-ready setups

  Capstone Projects: Real-World Applications

  We don’t just teach theory — we build. At the end of the course, you’ll complete 3 Capstone Projects that simulate real-world enterprise scenarios:

  • Capstone 1: Personal Research Assistant Agent
    • Given a topic or query, the agent autonomously gathers, summarizes, and synthesizes information from multiple sources and documents.
    • Uses ReAct reasoning, document retrieval via FAISS vector stores, LangChain tool orchestration, and memory management for contextual continuity.
    • Develop a Chat User Interface
    • Given a company name and documents, the agent performs autonomous research, summarization, SWOT analysis, and red-flag detection.
    • Uses tool orchestration, LangChain agents, document loaders, and vector store retrieval.
    • Develop a UI for the use case