Packt

Agent Architectures and Production Deployment with LangGraph

Packt

Agent Architectures and Production Deployment with LangGraph

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Understand the core principles of LangChain and LangGraph for agentic AI

  • Set up and configure APIs from OpenAI and Anthropic for seamless integration

  • Deploy AI agents to production environments with observability and monitoring

Details to know

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Recently updated!

October 2026

Assessments

4 assignments

Taught in English

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Build your subject-matter expertise

This course is part of the Agentic AI Systems: Build & Deploy with LangChain/LangGraph Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 4 modules in this course

This module provides a comprehensive exploration of LangGraph, focusing on building and managing stateful agentic workflows. Learners will gain hands-on experience with graph creation, dynamic routing, and human-in-the-loop integration. The content emphasizes practical implementation of complex AI systems using graph-based architectures.

What's included

19 videos1 reading1 assignment

This module explores the design and implementation of multi-agent systems using LangChain and LangGraph, covering key concepts like agent communication, coordination, and task distribution. Learners will gain hands-on experience with patterns such as ReAct, Map-Reduce, and message passing to build scalable and collaborative agent systems. The module also emphasizes best practices for error handling, state management, and hierarchical system design.

What's included

17 videos1 assignment

This module covers essential practices for deploying AI agents in production, including observability, security measures, testing strategies, and system resilience. Learners will gain hands-on experience with tools like LangSmith, LLM Guard, and FastAPI, while learning to implement robust error handling, caching, and monitoring solutions.

What's included

28 videos1 assignment

This module provides a comprehensive review of the key concepts covered in the course and offers practical guidance on how to continue developing and deploying agentic AI systems. Learners will gain insights into next steps for further learning and application.

What's included

1 video1 reading1 assignment

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