PHThe 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 ᑕᕼᗩTGᑭT, 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 casesPart 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 collisionsPart 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-offsPart 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 projectPart 5 - LangChain Fundamentals
Build your first production-quality AI applications.
Topics include
LangChain fundamentals LCEL (LangChain E×ρréššion Language) Chains Tools Tool Calling Context management Memory AI Agents Practical applicationsPart 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 interactionsPart 7 - No-Code & Low-Code Agent Development
Build AI applications visually.
Using
Langflow CrewAIProjects include
Research Assistant AI Travel Planner End-to-end AI workflowsPart 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 applicationsThis 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 resourcesWho 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:
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