Overview
Understand the core concepts and foundations of Agentic AI systems., Gain hands-on experience building AI agents using frameworks like LangChain, LangGraph and CrewAI., Learn to orchestrate tools, memory, and reasoning for enterprise-grade Agentic AI workflows., Monitor, evaluate, and productionize Agentic AI using real-world metrics and best practices using real world capstone projects., Build and deploy real-world AI agents using LangChain, LangGraph & CrewAI., Work on practical projects building AI agents with reasoning, planning & autonomy., Project1 - Build a Personal Research Assistant AI Agent that autonomously gathers, summarizes, and synthesizes data using ReAct, FAISS, LangChain, and memory., Project2 - Build an Investment Analyst AI Agent that researches companies, summarizes insights, performs SWOT analysis, and flags risks using LangChain tools
This course is designed for technology professionals, AI practitioners, and product builders who want to go beyond traditional LLM-based chatbots and build powerful Agentic AI systems that can reason, plan, act, and collaborate., It is ideal for:, AI/ML engineers looking to implement multi-agent systems and autonomous workflows., Backend and full-stack developers seeking to integrate LangChain, LangGraph, CrewAI, and ReAct-style agents into real-world applications., Tech founders and product managers who want to design scalable AI-powered workflows for enterprise or startup settings., Data scientists and architects interested in Retrieval-Augmented Generation (RAG), tool orchestration, monitoring, and agent observability., Advanced learners or researchers who are ready to explore cutting-edge architectures for AI decision-making, memory, and coordination.
Basic Python programming knowledge., Familiarity with REST APIs and JSON., Some exposure to LLMs (like OpenAI, Claude, etc.) is helpful but not mandatory., Familiarity with Ubuntu or any other Unix environment is preferred. Enterprise grade Agentic AI face some limitations in Windows environment.
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 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
Capstone 2: Investment Research Analyst Agent
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
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
Why Learn From This Instructor?
Your instructor is a seasoned AI consultant and product leader with decades of experience in building enterprise-scale AI solutions. He has architected GenAI systems across verticals including finance, compliance, ERP, edtech, and customer support, and is now sharing his battle-tested approach to Agentic AI design and deployment.
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.
Pranab Das
Pranab Das is a seasoned technology leader, AI strategist, and founder of Aigentic, a company pioneering the implementation of Agentic AI solutions for enterprises. With over two decades of experience across product development, digital transformation, and emerging technologies, Pranab brings a rare blend of technical depth, strategic thinking, and entrepreneurial execution to everything he builds.
His past leadership roles at organizations like Jio Platforms, Aliaxis, and EasyBuild have been instrumental in setting up technology-driven ventures and driving intrapreneurial innovation. At Aigentic, he is now spearheading a new wave of enterprise automation powered by Large Language Models (LLMs), multi-agent systems, and GenAI stacks.
Pranab has a strong track record of applying AI to real-world domains such as ERP automation, customer support, startup due diligence, and skills education. As the creator of the EAGLE framework (Enterprise Augmented GenAI & LLM Engine), he has designed modular agentic systems tailored for various verticals including education, finance, HR, manufacturing, and agritech.
He is passionate about demystifying advanced AI concepts for learners and helping professionals build hands-on agentic applications using LangChain, CrewAI, LangGraph, AutoGen, and other leading frameworks. His teaching style focuses on bridging conceptual clarity with practical coding experience, empowering students to confidently step into the GenAI era.
Whether you're a developer, product manager, or founder, Pranab’s courses are designed to help you understand, design, and deploy AI Agents that can reason, act, and collaborate autonomously.