Skills vs MCP vs Agents: The Future of AI (Explained Simply)

Part 1 - LangChain Foundations: Understanding LLMs and Orchestration

A beginner-friendly introduction to LLMs, why we need LangChain, and how LangGraph helps build workflow-based AI systems.

Part 2 - LangChain Essentials: Building Blocks of AI Applications

Learn the core LangChain building blocks - chat models, prompting patterns, structured outputs, and chaining - explained simply.

Part 3 - Tool Calling: Giving Your AI Real-World Superpowers

Learn how to extend LLMs with real-world capabilities - from calling APIs to using MCP adapters - explained in simple terms.

Part 4 - RAG Basics: Teaching AI About Your Documents

Learn how to build RAG (Retrieval Augmented Generation) systems that let AI answer questions from your documents - explained simply.

Part 5 - LangGraph & Agents: Building Intelligent Workflows

Learn how to build sophisticated agentic systems with LangGraph using states, nodes, edges, and workflows - explained simply.

Part 6 - Project: Build Your Own AI Agent

Step-by-step guide to building a complete agentic application with tools, memory, and workflows - a hands-on project for beginners.

Part 7 - Observability with LangSmith: Debug and Monitor Your AI

Learn how to use LangSmith to debug, trace, and monitor your AI applications - find issues fast and optimize performance.

Saturday Morning Coffee ☕ Thoughts on Agentic AI

Why Agentic AI isn't failing because it's weak it's failing because we're asking it to do the wrong job. Agents should orchestrate workflows, not replace them.

Case Study: Amazon Rufus - AI Shopping Assistant Done Right

How Amazon's Rufus demonstrates the right way to build AI assistants - agents orchestrate, pipelines execute, policies enforce.