Agentic AI From Foundations to Enterprise - Grade Systems

[dkmdkm]

Free Download Agentic AI From Foundations to Enterprise-Grade Systems
Published 10/2025
Created by Pranab Das
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 54 Lectures ( 9h 43m) | Size: 4.32 GB

Learn Agentic AI from scratch: Build ReAct Agents, use tools, manage memory, and deploy real-world systems
What you'll learn
Understand the core concepts and foundations of Agentic AI systems.\nGain hands-on experience building AI agents using frameworks like LangChain, LangGraph and CrewAI.\nLearn to orchestrate tools, memory, and reasoning for enterprise-grade AI workflows.\nMonitor, evaluate, and productionize Agentic AI using real-world metrics and best practices using real world capstone projects.
Requirements
Basic Python programming knowledge.\nFamiliarity with REST APIs and JSON.\nSome exposure to LLMs (like OpenAI, Claude, etc.) is helpful but not mandatory.\nFamiliarity 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 SystemsCourse OverviewWelcome 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 LearnThe fundamentals of Agentic AI and how it differs from traditional prompt engineeringCore architectural patterns like the ReAct pattern (Reasoning + Acting)How to build a minimal ReAct agent from scratch in PythonHow to integrate tools like web search, calculators, databases, APIs, and custom functionsImplementing multi-turn reasoning and agent tool-chainingHandling errors, timeouts, and tool failures gracefullyAdding logging, monitoring, and agent evaluation capabilitiesArchitecting hierarchical agents, multi-agent collaborations, and role-based delegationDesigning and deploying enterprise-grade agents with:LangChainLangGraphCrewAIFAISS Vector StoresOpenAI & Hugging Face ModelsFastAPI / FlaskCloud / On-Prem Deployment-ready setupsCapstone Projects: Real-World ApplicationsWe 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 AgentGiven 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 InterfaceCapstone 2: Investment Research Analyst AgentGiven 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 caseTechnologies & Frameworks CoveredAgentic Design Patterns: ReAct, Hierarchical AgentsLLMs: OpenAI (GPT-4, GPT-3.5), Hugging Face TransformersFrameworks: LangChain, LangGraph, CrewAIMemory Architectures: Short-term, Long-term, Vector Store Memory (FAISS, ChromaDB)Tool Integration: APIs, Web Search, Calculators, Custom ToolsVector Databases: FAISS, BM25 hybrid retrievalServer Frameworks: FastAPI, FlaskUI: StreamlitDeployment Options: On-Premise, Cloud, Dockerized setupsMonitoring & Logging: Custom logging, Agent behavior evaluation, Prometheus, GrafanaError Handling: Graceful fallbacks, retry logic, observation parsingWhy 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 promptingBackend Developers interested in building LLM-powered systemsProduct & Tech Leads building AI-first productsEnterprise Architects designing GenAI agent stacksHackathon teams and startup buildersOutcomes You Can ExpectBy the end of the course, you will:Understand how to build intelligent, goal-driven agentsGain hands-on experience with real-world tools & vector searchBuild multi-step reasoning flows with LangChain & LangGraphDeploy scalable, production-ready agent architecturesBe confident to apply Agentic AI in enterprise use casesKey FeaturesMany hands-on code examplesable templates and prompt formatsCapstone projects with real-world contextModular code that you can reuse and extendTake 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.\nIt is ideal for:\nAI/ML engineers looking to implement multi-agent systems and autonomous workflows.\nBackend and full-stack developers seeking to integrate LangChain, LangGraph, CrewAI, and ReAct-style agents into real-world applications.\nTech founders and product managers who want to design scalable AI-powered workflows for enterprise or startup settings.\nData scientists and architects interested in Retrieval-Augmented Generation (RAG), tool orchestration, monitoring, and agent observability.\nAdvanced learners or researchers who are ready to explore cutting-edge architectures for AI decision-making, memory, and coordination.
Homepage

Bitte Anmelden oder Registrieren um Links zu sehen.

Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

No Password - Links are Interchangeable

 

[0dayddl]

Agentic AI From Foundations to Enterprise-Grade Systems
Published 10/2025
Duration: 9h 44m | .MP4 1280x720 30 fps(r) | AAC, 44100 Hz, 2ch | 4.32 GB
Genre: eLearning | Language: English​

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.

Bitte Anmelden oder Registrieren um Links zu sehen.

RapidGator

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

DDownload

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!