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The Complete Agentic AI Course
Published 7/2026
Created by Pankaj Kang, Atul Krishna Singh
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 99 Lectures ( 16h 11m ) | Size: 13.1 GB
From LLM Fundamentals to Production-Ready AI Agents

What you'll learn

Build a strong foundation in Generative AI, Large Language Models (LLMs), and understand how modern AI systems work.

Work confidently with leading LLM APIs to build real-world AI applications.

Master Prompt Engineering techniques including zero-shot, few-shot, role prompting, prompt templates, evaluation, and optimization.

Understand the complete architecture and execution lifecycle of Agentic AI systems.

Design AI agents that can reason, plan, use tools, maintain memory, and complete multi-step tasks autonomously.

Compare and choose the right AI agent frameworks for different use cases and application Requirements

Build intelligent AI applications using LangChain, including chains, tools, tool calling, context management, memory, and AI agents.

Implement advanced Agentic AI capabilities such as long-term memory, middleware, guardrails, planning, deep agents, subagents, and multi-agent systems.

Build complete no-code and low-code AI applications using Langflow and CrewAI.

Evaluate, monitor, and improve AI applications using LangSmith, MLflow, and modern evaluation methodologies.

Apply industry best practices for developing reliable, scalable, and production-ready AI systems.

Build multiple hands-on projects that demonstrate real-world Agentic AI development skills.

Confidently design, develop, and deploy intelligent AI solutions for practical business and personal use cases.

Gain the knowledge and practical experience needed to transition from using AI tools to building your own production-ready AI agents.
Requirements

Basic Python programming knowledge (variables, functions, loops, and classes).

Familiarity with using a code editor such as Visual Studio Code or Jupyter Notebook.

We'll start from the fundamentals and progressively build toward designing, developing, and evaluating production-ready AI agents, making the course suitable for both beginners and experienced developers looking to enter the field of Agentic AI.
Description
Artificial Intelligence is evolving faster than ever before. We've moved from traditional machine learning to Large Language Models, and now we're entering the era ofAgentic AI-where AI systems don't just generate responses, but can reason, plan, use tools, maintain memory, collaborate with other agents, and autonomously solve complex real-world problems.
If you want to understand how modern AI applications like ChatGPT, Claude, Cursor, GitHub Copilot, Deep Research, and other intelligent assistants are built, this course is designed for you.
Unlike courses that focus only on a single framework or a collection of code snippets, this course follows a carefully structured learning path. You'll start by building a strong foundation in Generative AI and Large Language Models, then master Prompt Engineering, understand the principles behind AI agents, and finally build production-ready Agentic AI applications using today's leading frameworks and tools.
Throughout the course, you'll gain both conceptual understanding and hands-on experience by building practical projects using real APIs and industry-standard tools.
What you'll learn
Part 1 - Foundations of Generative AI
Build a solid understanding of how modern AI works.
You'll learn

What Generative AI is

Evolution of AI and Large Language Models

Open-source vs. closed-source models

Tokenization, embeddings, attention, and context windows

Inference, temperature, latency, throughput, and model parameters

Training vs. fine-tuning

Working with LLM APIs

Structured Outputs

Choosing the right model for different use cases
Part 2 - Prompt Engineering
Learn how to communicate effectively with Large Language Models.
You'll master

Prompt engineering fundamentals

Zero-shot, one-shot, and few-shot prompting

Role and context engineering

Prompt templates

Parameterized prompts

Model-aware prompting

Prompt optimization

Prompt evaluation

A/B testing

Regression testing

Prompt monitoring

Common failure modes such as hallucinations, prompt brittleness, and context collisions
Part 3 - Introduction to Agentic AI
Understand what AI agents actually are before using any framework.
Topics include

Chatbots vs. workflows vs. AI agents

Static vs. adaptive systems

Agent autonomy

Agent execution loop

Planning

Reasoning

Feedback loops

Tools

Memory

State management

Environment interaction

Architectural trade-offs
Part 4 - Agent Framework Ecosystem
Explore the modern AI agent landscape.
You'll discover

Why agent frameworks exist

The AI technology stack

Popular agent frameworks

Code-based vs. low-code vs. no-code solutions

Selecting the right framework for your project
Part 5 - LangChain Fundamentals
Build your first production-quality AI applications.
Topics include

LangChain fundamentals

LCEL (LangChain Expression Language)

Chains

Tools

Tool Calling

Context management

Memory

AI Agents

Practical applications
Part 6 - Advanced LangChain Concepts
Go beyond basic agents and learn advanced capabilities.
You'll build applications using

Long-Term Memory

Middleware

Guardrails

Planning

Multi-Agent Systems

Deep Agents

Subagents

Filesystem interactions
Part 7 - No-Code & Low-Code Agent Development
Build AI applications visually.
Using

Langflow

CrewAI
Projects include

Research Assistant

AI Travel Planner

End-to-end AI workflows
Part 8 - Observability, Evaluation & Prompt Management
Learn how production AI systems are monitored and improved.
You'll cover

LangSmith

MLflow

AI Evaluation

Evaluation methodologies

Prompt management

Monitoring AI applications
This Course Includes

Comprehensive coverage from fundamentals to advanced Agentic AI

Hands-on coding exercises

Real-world projects

Practical notebooks

Modern AI frameworks

Best practices used in industry

Step-by-step explanations

Production-focused concepts

able resources
Who this course is for

Software Engineers who want to build intelligent AI applications and autonomous AI agents.

Data Scientists and Machine Learning Engineers looking to expand into Generative AI and Agentic AI.

Python Developers interested in integrating Large Language Models (LLMs) into real-world applications.

AI Engineers who want to master LangChain, AI agents, memory, tool calling, and production-ready AI systems.

Students and researchers who want a structured, hands-on introduction to modern AI technologies.

Developers interested in building AI assistants, copilots, research agents, workflow automation, and multi-agent systems.

Professionals looking to upskill in one of the fastest-growing areas of Artificial Intelligence.

Anyone who understands basic Python and wants to move beyond using AI tools to building their own intelligent AI applications.
Homepage

Code:

https://www.udemy.com/course/the-complete-agentic-ai-course

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The Complete Agentic AI Course

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