Agentic AI Masterclass (Full Series) | Build Autonomous AI Agents with LangChain & Python

Description

Welcome to the Agentic AI Masterclass — a complete, end-to-end series on designing, building, and scaling autonomous AI agents using modern LLM frameworks like LangChain. This masterclass takes you from first principles to production-ready agentic systems, covering both theory and real implementations. You’ll learn how AI agents go beyond chatbots to become goal-driven, tool-using, self-improving systems capable of reasoning, planning, routing, and collaboration. 🚀 What This Full Series Covers: What Agentic AI really is (beyond LLMs) Core agent loop: perceive → reason → plan → act → reflect Agentic design patterns for scalable systems Levels of AI agents: from single LLMs to multi-agent teams Future of AI agents: proactive, embodied, and goal-driven systems Prompt Chaining (theory + LangChain implementation) Context Engineering for high-accuracy agents Routing & Dynamic Routing in agent workflows Parallelization pattern for faster agent execution Reflection pattern for self-improving AI systems Tool / Function calling in agentic architectures 🧠 Who This Is For: AI Engineers & ML Developers LangChain & LLM practitioners Researchers exploring autonomous systems Anyone building real-world AI agents, not just demos By the end of this masterclass, you’ll have a clear mental model and hands-on experience building robust, modular, and intelligent agentic systems. Chapters: Welcome and Introduction to Agentic AI Masterclass (0:00) What This Full Series Covers (0:40) Who This Is For (1:20) Agentic AI Era and Goal-Driven Systems (16:32) Four Distinct Levels of AI Agent Complexity (17:33) Level Zero: The LLM as a Reasoning Engine (18:10) Level One: LLM as an Active Problem Solver with Tools (19:21) Level Two: Strategic Problem Solving and Planning (20:42) Level Three: Multi-Agent Collaboration and Automation (22:15) Future of AI Agents: Autonomous and Collaborative Ecosystems (23:56) Understanding Prompt Chaining (31:52) Key Failure Modes of Single Prompts (32:31) Why Prompt Chaining is the Solution (35:31) Reliability in Prompt Chaining: Tool Integration (36:35) Reliability in Prompt Chaining: Consistent Output (37:03) Practical Applications of Prompt Chaining: Information Processing (38:20) Practical Applications of Prompt Chaining: Complex Query Answering (39:11) Practical Applications of Prompt Chaining: Content Creation (41:42) Practical Applications of Prompt Chaining: Multi-Turn Dialogues (42:16) Practical Applications of Prompt Chaining: Complex Programming Tasks (42:56) Practical Applications of Prompt Chaining: Diverse Data Types (43:40) Building a Two-Step Pipeline with LangChain (44:31) Creating a Python Virtual Environment (45:37) Setting Up requirements.txt and Installing Dependencies (46:26) Securing API Keys with .env File (48:01) Implementing the Prompt Chaining Logic (app.py) (49:27) Running the Prompt Chaining Code and Observing Output (56:36) Introduction to Context Engineering (58:42) Why Context Matters in AI Output (59:23) The Future of AI and Context Engineering (1:03:09) Introduction to Routing in AI Agents (1:03:36) Types of Routing: Embedding-Based Routing (1:07:08) Types of Routing: Rule-Based Routing (1:08:40) Types of Routing: LLM-Based Routing (1:10:28) Dynamic Routing at Multiple Stages (1:11:18) Importance of Dynamic Routing in AI Agents (1:15:06) Implementing Routing Using LangChain (1:18:04) Setting Up Virtual Environment and Requirements for Routing (1:19:03) Importing LangChain and Google Generative AI (1:19:42) Defining Handler Functions for Different Request Types (1:22:48) Defining Branches with RunnablePassthrough and RunnableBranch (1:27:26) Setting Up the Coordinator Agent for Routing (1:29:19) Testing the Routing System with Sample Requests (1:30:15) Understanding the Parallelization Pattern (1:32:12) Applications of Parallelization: Input Validation (1:40:42) Applications of Parallelization: Multi-Option Generation (1:41:13) Conclusion on Parallelization (1:41:50) Introduction to the Reflection Pattern (1:43:41) Importance of Memory in the Reflection Pattern (1:47:01) Building a Reflection App in Python (1:51:03) Stage 1: Generating and Refining Code (Producer Agent) (1:56:52) Stage 2: Reflecting on Generated Code (Critic Agent) (1:59:05) Implementing Stopping Condition for Reflection Loop (2:01:05) Running the Reflection Code and Analyzing Output (2:03:13) Summary of Reflection Pattern Effectiveness (2:06:52) Tool Calling in AI Agents (2:16:32) Why Tools are Essential for Language Models (2:17:02) Implementing Tool Use with LangChain: Two-Stage Process (2:17:19) Setting Up Environment for Tool Calling (2:17:55) Defining Custom Tools (Multiply and Add Functions) (2:20:00) Running Tool Calling Code and Debugging (2:24:23) Demonstrating Tool Call in Action (2:28:40) Conclusion on Tool Use and Agent Capabilities (2:32:54) 📌 This is not hype — this is real agentic engineering.