Building AI Agents with LangGraph

Name: Building AI Agents with LangGraph Course
Rating: 5 (12 reviews)

This course guides learners through the essentials of implementing intelligent agents using LangGraph. Learners will explore external tools and APIs, and learn how to integrate them effectively within their agents. Key lessons include interacting with databases and implementing LangGraph Database Agents for efficient data retrieval. The course covers advanced topics like Retrieval Augmented Generation, incorporating human-in-the-loop strategies, and ensuring agent observability and reliability. Finally, learners will apply these concepts in a hands-on project, designing an Energy Advisor agent, which synthesizes their knowledge into a practical application.

Course

Intermediate
8 hours
Updated: Mar 31, 2026

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Hands-on projects with expert feedback
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Program Certificates

Skills you'll learn

12 skills

Implementing Human-in-the-Loop Agents
Implementing Single-agent RAG
Single-Agent Retrieval Augmented Generation
Implementing API Integration for Agents
Implementing AI Agent Evaluation

Prerequisites

2 prerequisites

Prior to enrolling, you should have the following knowledge:

Basic LangChain
Intermediate Python

You will also need to be able to communicate fluently and professionally in written and spoken English.

Course Outline

13 lessons
1 project

Building AI Agents with LangGraph

Introduction
Meet your instructor and get an overview of the course on building agentic systems in LangGraph.
External Tools and APIs
Explore using external APIs for real-time data, dynamic actions, and authenticating agents. Discover MCP, a protocol standardizing AI’s tool interoperability and safety.
Integrating External Tools and APIs with LangChain
Learn to integrate external APIs as tools in LangChain via LangGraph, enabling agents to fetch real-time data and enrich LLM responses using structured workflows and router logic.
Interacting with Databases
Equip agents to access and modify structured data by using SQL for interaction and vector databases for semantic tasks, ensuring seamless integration with private systems.
LangGraph Database Agents
Learn to build LangGraph database agents that use AI to convert natural language into SQL queries, enabling users to query databases conversationally without writing SQL.
Agentic Retrieval Augmented Generation
Discover Agentic RAG: Enhance RAG by enabling reflection, query reformulation, and intelligent adaption for nuanced answers. Master retrieval, reasoning, and retry loops.
Single-Agent Retrieval Augmented Generation in LangGraph
Explore single-agent Retrieval-Augmented Generation in LangGraph, adding agentic decision-making for dynamic web search and improved knowledge-grounded AI responses.
Human-in-the-Loop & Observability
Explore human-in-the-loop and observability techniques to monitor, debug, and optimize AI agents, ensuring oversight, transparency, and reliable performance at scale.
LangGraph Agent Observability
Learn how to use MLflow with LangGraph and LangChain to trace, log, and inspect each step of agent workflows, enabling better debugging, transparency, and observability.
LangGraph Agents with Human-in-the-Loop
Explore how to add human approvals or edits at key points in LangGraph agent workflows, enhancing safety and control with human-in-the-loop techniques.
Agent Reliability & Evaluation
Discover how to build reliable AI agents by defining success metrics, effective evaluation, continuous monitoring, and applying best practices for trustworthiness and real-world impact.
LangGraph Agent Evaluation
Learn to build, evaluate, and benchmark RAG pipelines and LangGraph agent workflows using RAGAS and MLflow for quality and correctness assessment.

Project: Energy Advisor
In this project, you’ll build an AI agent with LangChain to optimize EcoHome energy use, integrating APIs, a database, and vector search, with LangGraph orchestration and evaluation.

Program Instructors

1 instructor

Unlike typical professors, our instructors come from Fortune 500 and Global 2000 companies and have demonstrated leadership and expertise in their professions:

Henrique Santana

Principal Machine Learning Engineer at Dell Technologies

Henrique Santana

Principal Machine Learning Engineer at Dell Technologies

About this program

Gain advanced skills designing autonomous AI agents with LangChain and LangGraph. Implement RAG workflows, human-in-the-loop oversight and agent observability.

Subscription · Monthly

Cancel Anytime
Unlimited access to hundreds of top-rated courses
Hands-on projects with expert feedback
Personalized career coaching and interview prep
Program Certificates

Building AI Agents with LangGraph

Skills you'll learn

Prerequisites

Course Outline

Program Instructors

About this program

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