Building AI Agents with LangGraph

Overview

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.

Syllabus

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.