Agentic AI Tutorial - Agentic AI From Foundations to Enterprise-Grade Systems

Description

https://www.udemy.com/course/agentic-ai-from-foundations-to-enterprise-grade-systems Agentic AI: From Foundations to Enterprise-Grade Systems Course Overview 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 / ChromaDB 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 2 Capstone Projects that simulate real-world enterprise scenarios: Capstone 1: Personal Research Assistant Agent o Given a topic or query, the agent autonomously gathers, summarizes, and synthesizes information from multiple sources and documents. o Uses ReAct reasoning, document retrieval via FAISS vector stores, LangChain tool orchestration, and memory management for contextual continuity. o Develop a Chat User Interface Capstone 2: Investment Research Analyst Agent o Given a company name and documents, the agent performs autonomous research, summarization, SWOT analysis, and red-flag detection. o Uses tool orchestration, LangChain agents, document loaders, and vector store retrieval. o Develop a UI for the use case Technologies & Frameworks Covered Agentic Design Patterns: ReAct, Hierarchical Agents LLMs: OpenAI (GPT-4, GPT-3.5), Hugging Face Transformers Frameworks: LangChain, LangGraph, CrewAI Memory Architectures: Short-term, Long-term, Vector Store Memory (FAISS, ChromaDB) Tool Integration: APIs, Web Search, Calculators, Custom Tools Vector Databases: FAISS, BM25 hybrid retrieval Server Frameworks: FastAPI, Flask UI: Streamlit Deployment Options: On-Premise, Cloud, Dockerized setups Monitoring & Logging: Custom logging, Agent behavior evaluation, Prometheus, Grafana Error Handling: Graceful fallbacks, retry logic, observation parsing Who Is This Course For? This course is ideal for: • AI/ML Developers who want to go beyond prompting • Backend Developers interested in building LLM-powered systems • Product & Tech Leads building AI-first products • Enterprise Architects designing GenAI agent stacks • Hackathon teams and startup builders Outcomes You Can Expect By the end of the course, you will: • Understand how to build intelligent, goal-driven agents • Gain hands-on experience with real-world tools & vector search • Build multi-step reasoning flows with LangChain & LangGraph • Deploy scalable, production-ready agent architectures • Be confident to apply Agentic AI in enterprise use cases Key Features • Many hands-on code examples • Downloadable templates and prompt formats • Capstone projects with real-world context • Modular code that you can reuse and extend Take your AI development skills to the next level — Enroll now and start building agents that think, act, and scale.