Build Real AI Agents with LangChain
AI agents are programs that use large language models as a reasoning core — they plan, call tools, retrieve data, and loop until a task is complete, without a human directing each step. LangChain is the most widely adopted framework for wiring these components together in Python. This track teaches you to build, debug, and ship autonomous workflows that go well beyond a single prompt-response call.
What You Will Learn
You will start with how agents reason and how LangChain organizes that reasoning, then move into prompt engineering and model integration so your agents produce reliable output. Core topics include building chains and sequential workflows, attaching tools and toolkits so agents can call APIs and run code, and managing memory so an agent retains context across steps. You will cover data loading and retrieval for RAG pipelines, observability and debugging to understand what an agent actually did, and custom tool creation so you can connect any external service. Advanced sections address multi-agent and hierarchical architectures, autonomous workflow orchestration, and production deployment with scaling considerations.
The Learning Path
Twelve courses span A1 through C1. The track opens with Introduction to AI Agents & LangChain Fundamentals (A1, free) and moves through prompt engineering, chains, and the tools-and-agents layer at B1. The B2 block deepens the picture with memory management, retrieval, observability, and custom integrations. The track closes at C1 with Advanced Agent Architectures, Autonomous Workflow Orchestration, and Production Deployment & Scaling — the courses where individual components are combined into systems that run unattended in production.
How It Works
Each course is broken into short, focused lessons you complete in the built-in code editor with real-time feedback. An AI tutor is available whenever you get stuck, and every exercise runs against real LangChain code so you see actual agent behavior rather than toy examples.
