Agentic AI Engineer with LangChain and LangGraph

Name: Agentic AI Engineer with LangChain and LangGraph Nanodegree Program
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Agentic AI Engineer with LangChain and LangGraph is a program that teaches Python developers how to turn large‑language‑model applications into fully autonomous agents.  You begin with LangChain fundamentals—prompt templates, chains, memory, and single‑tool agents—then progress to multi‑tool planning, self‑critique loops, and deployment practices. Finally, you integrate external knowledge through retrieval‑augmented generation, long‑term memory, and multi‑agent collaboration.

Nanodegree Program

Intermediate
26 hours
Updated: Jan 28, 2026

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Skills you'll learn

39 skills

Multi-Agent Implementation
Implementing Multi-Agent Orchestration
Multi-Agent Architecture in Python
Long-term Memory Management for AI Agents
Implementing Multi-Agent Architectures

Prerequisites

2 prerequisites

Prior to enrolling, you should have the following knowledge:

Large Language Models
Intermediate Python

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

Program Outline

3 courses
40 lessons
3 projects

Course 1: LangChain Agentic AI Fundamentals

This course provides a comprehensive overview of building intelligent agentic applications using LangChain. Participants will learn to create simple LangChain applications and explore structured outputs. The curriculum progresses through multi-step workflows, transitioning from LLM calls to developing agents, and covers essential agentic design patterns. Key concepts include extending agents with tools, managing functions, and understanding state management within LangGraph. Students will also implement short-term agent memory techniques and complete a hands-on project: building a report-generating agent.

9 hours

Course Introduction & Setup
Get introduced to the course, meet your instructors, and learn how to set up and use your Vocareum OpenAI API key and assigned budget for hands-on activities.
Creating a Simple LangChain application
Learn to build a simple LangChain app: integrate LLMs, manage chat history, use prompt templates, and apply few-shot prompting to create customizable chatbots with memory.
Structured Outputs
Discover structured outputs in AI: transform responses into actionable JSON for integration. Utilize schemas, parsers, and function calls to enhance reliability and automation in workflows.
LangChain Structured Outputs
Learn to parse and structure LLM outputs in LangChain using output parsers, TypedDict, Pydantic models, and automatic error correction for robust workflows.
Multi-Step Workflows in LangChain
Learn to build flexible, multi-step AI workflows in LangChain using Runnables and LCEL for composing, batching, and managing complex chains with ease and scalability.
From LLM Calls to Agents
Explore how AI evolves from basic LLM calls to fully autonomous agents, learn agentic frameworks, applications, and how to design effective agent-driven workflows.
Agentic Design Patterns
Discover four key agentic design patterns—reflection, tool use, planning, and multi-agent collaboration—to boost LLM performance and reliability in complex tasks.
Extending Agents with Tools
Extend AI agents beyond text with tool integrations, enabling reliable real-time actions and data access.
Functions as Tools in LangChain
Learn how to encapsulate Python functions as tools in LangChain, enabling LLMs to call functions, interact with APIs, and handle external computations in AI workflows.
Agentic Workflows with LangGraph
Learn to build dynamic, agent-driven AI workflows using LangGraph, leveraging nodes, edges, and routing for modular, adaptive application control and automation.
Agent State Management
Explore agent state management with state machines. Learn how agents track user input, instructions, and tool use for complex workflows, ensuring adaptability and reliability.
LangGraph State Management
Learn structured state management in LangGraph for robust AI workflows, with focus on TypedDicts, reducers, routing, handlers, persistence, and practical multi-turn agent design.
Short-Term Agent Memory
Explore short-term memory in AI agents, enhancing coherence via state, ephemeral, and ephemeral memory strategies for efficient context retention in active sessions.
LangGraph Agent Memory
Learn how LangGraph uses checkpointers and threads for agent memory, allowing persistent, isolated, and customizable conversations with snapshots, time travel, and personalized assistance.

Project: Report-Building Agent
Build a document processing system using LangChain/LangGraph. You'll create an AI assistant that answers questions, summarize documents, and perform calculations on financial and healthcare documents.

Course 2: Building AI Agents with LangGraph

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.

8 hours

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.

Course 3: Advanced Agentic AI Techniques

This course equips learners with the essential skills and knowledge to design and implement sophisticated agent-based systems. The course covers long-term memory integration within agents, emphasizing the LangGraph framework. Participants explore multi-agent architectures and state management, focusing on effective orchestration and data routing. Through hands-on projects, learners will implement agentic systems, culminating in the development of an Autonomous Knowledge Agent.

9 hours

Course Introduction
Meet your instructors and get an overview of advanced agentic systems and course structure.
Long-Term Agent Memory
Explore long-term agent memory: understand semantic, episodic, and procedural memories. Learn storage strategies and best practices for personalized, coherent interactions.
Long-Term Agent Memory in LangGraph
Learn how to persist agent memory in LangGraph using databases like SQLite and enhance long-term AI memory with vector storage via LangMem for robust, session-aware agents.
Designing Multi-Agent Architecture
Explain the core components of multi-agent systems and how to design their high-level architecture.
Designing Multi-Agent Architectures with LangGraph
Explore foundational multi-agent architecture patterns using LangGraph. Design, implement, and visualize orchestrated and peer-to-peer agent workflows for real-world scenarios.
State Management in Multi-Agent Systems
Evaluate methods for tracking and updating agent state across multi-turn interactions.
Implementing Multi-Agent Architectures with LangGraph
Learn to design, implement, and orchestrate multi-agent workflows using LangGraph for structured, auditable, and automated content pipelines with specialized agents and custom state.
Orchestrating Agent Activities
Apply orchestration techniques to coordinate multiple agent actions and achieve complex workflows.
Orchestrating Agent Activities with LangGraph
Learn to orchestrate multi-agent workflows with LangGraph, using supervisors, handoffs, tool-calling, and structured state management for scalable, modular agent systems.
Routing and Data Flow in Agentic Systems
Configure routing mechanisms to manage data flow among agents in multi-agent systems.
Implementing Data Routing in Agentic Systems with LangGraph
Learn to implement content-based, round-robin, and priority-based data routing in agentic systems using LangGraph, Pydantic, and LangChain frameworks.
Building Agents Project: Autonomous Knowledge Agent
In this project, you will develop UDA-Hub, an intelligent, multi-agent decision suite capable of resolving customer support tickets across multiple platforms.

Skills that demand high salaries

According to the US Bureau of Labor Statistics, careers in artificial intelligence are projected to grow 21% from 2021 to 2031.*

AI Engineer

Salary info from Talent.com

Low
$134,180
Average
$163,152
High
$208,800

Program Instructors

4 instructors

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

Gerald Parker

AI Engineer at Brevity Pitch

Christopher Agostino

Founder and Research Scientist at NPC Worldwide

Joshua Bernhard

Staff Data Scientist at Marketplace