• views: 114
• date: 7 November 2025
• posted by: AD-TEAM

Build Agentic AI with LangChain, LangGraph & CrewAI — create ReAct Agents, use tools, and manage memory.

What you'll learn
- 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 AI workflows.
- Monitor, evaluate, and productionize Agentic AI using real-world metrics and best practices using real world capstone projects.

Requirements
- 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.

Description
Agentic AI: From Foundations to Enterprise-Grade Systems

Course Overview

Welcome toAgentic AI: From Foundations to Enterprise-Grade Systems— yourcomplete hands-on guide to designing, building, and deploying intelligent AI agentsfor real-world applications.

This course is built fordevelopers, AI enthusiasts, and enterprise architectswho want to go beyond prompting and explore theagentic capabilities of modern LLMs(Large Language Models).

You'll learnhow to structure AI agents, empower them withtools, manage theirmemory and state, and evolve them intoenterprise-grade, multi-agent systems.

What You Will Learn

The fundamentals ofAgentic AIandhow it differs from traditional prompt engineering

Core architectural patterns like theReAct pattern(Reasoning + Acting)

How to build aminimal ReAct agentfrom scratch in Python

How to integratetoolslike web search, calculators, databases, APIs, and custom functions

Implementingmulti-turn reasoningand agent tool-chaining

Handlingerrors,timeouts, andtool failuresgracefully

Addinglogging,monitoring, andagent evaluationcapabilities

Architectinghierarchical agents,multi-agent collaborations, androle-based delegation

Designing and deployingenterprise-grade agentswith:

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 — webuild. At the end of the course, you'll complete3 Capstone Projectsthat 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.

Usestool orchestration,LangChain agents,document loaders, andvector 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 aseasoned AI consultant and product leaderwith decades of experience in buildingenterprise-scale AI solutions. He has architected GenAI systems across verticals includingfinance,compliance,ERP,edtech, andcustomer support, and is now sharing hisbattle-tested approachtoAgentic 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 buildingAI-first products

Enterprise Architects designingGenAI 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 inenterprise 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.

Who this course is for:
- 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.
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