Course Overview
This course teaches how to build reliable AI agents using LangChain from scratch. It covers chat models, structured output, tool calling, guardrails, and tracing to create production-ready agents that can perform multi-step tasks with real tools. The course emphasizes practical building and debugging techniques to ensure agents return clean, validated data and handle failures gracefully.
Key Takeaways
- Build a complete LangChain agent that calls real tools for multi-step tasks.
- Control LLM behavior using system prompts and structured output for validated results.
- Add guardrails and tracing to catch unsafe actions and debug failures.
- Apply architecture principles to create production-ready agents.
- Understand AI agent concepts including the reason–act–observe loop and when to use plain LLM calls.
- Define custom LangChain tools and understand agent decision-making for tool usage.
- Make tool calling reliable by understanding its internal workings and common failure points.
- Recognize common reasons for agent project failures and design to avoid them.
Prerequisites
- Working knowledge of Python (functions, classes, dictionaries, JSON).
- An API key for an LLM provider such as OpenAI or Anthropic (free tiers suffice).
- Python 3.10+ installed with a code editor.
- No prior experience with LangChain or AI agents is required.
Target Learners
- Python developers building their first AI agent.
- Software engineers moving beyond simple ChatGPT-style API calls to agents that reason and act.
- Backend developers needing agents that return structured, predictable output for integration.
- Anyone who has tried building an agent and wants to learn how to make one reliable.
- Anyone seeking a short, focused introduction to LangChain agents.
Welcome to the Course: Building AI Agents with LangChain
0:27
What Is an AI Agent? The Core Idea Behind the Hype
9:39
Inside the Agent Loop: Reason, Act, Observe
10:48
Agent or Plain LLM Call? How to Choose the Right Tool for the Task
5:46
The Top Reasons Agents Break in Production — and How to Avoid Them
12:41
AI Agent Fundamentals — A Quick Reference
19:50
Working with LangChain Chat Models: System Prompts and Structured Output
22:44
Hands-On: Your First LangChain Agent That Returns Structured Data
13:54
LangChain Building Blocks — Messages, Prompts and Output Schemas
11:05
Introduction to LangChain Tools: How Agents Take Action
11:14
Tool Calling Under the Hood: Making It Reliable
19:52
Hands-On: Building a LangChain Agent Equipped with Real Tools
5:51
Designing Tools Agents Can Use Correctly
12:15
Agent Architecture Principles That Survive the Real World
14:40
Adding Guardrails and Tracing to Your LangChain Agent
14:33
Hands-On: Assembling a Production-Ready LangChain Agent
8:10
Putting It Together — Tools, Reliability and Architecture
12:28
Course Wrap-Up & Next Steps
0:19
Access Files
No pi required.
Free
Course files
1 file package
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Full Pack
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