Back to Catalog

Make Your AI Agents Smarter with Reflection in LangGraph

IntermediateGuided Project

Build AI Agents that reason, critique their own work, and iteratively improve their responses just like a human. This project teaches you to build a tweet optimization workflow where content improves with each iteration, just like human editing. In just 45 minutes, master the principles of reflection agents that can recognize their own limitations and actively improve their responses—an essential skill for creating more intelligent, agentic AI systems.

Language

  • English

Topic

  • Artificial Intelligence

Skills You Will Learn

  • AI Agent, LLM, LangGraph, Python, Generative AI

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 45 minutes

Platform

  • SkillsNetwork

Last Update

  • April 24, 2025
About this Guided Project
What separates sophisticated AI from basic models? The ability to critique and improve its own work. In this guided project, you'll learn how to implement reflection agents using LangGraph—AI systems that evaluate their outputs, identify weaknesses, and iteratively enhance their responses without human intervention.

You'll construct a complete workflow that mimics the human creative process: generate content, step back to evaluate it, then revise based on deliberate feedback. Working with a practical tweet generation example, you'll witness how reflection transforms basic AI responses into polished, engaging content through multiple refinement cycles.

This project reveals the architecture behind self-improving agentic AI systems, giving you hands-on experience with one of the most important advancements in modern AI development—the ability to implement artificial "System 2" thinking where deliberate reflection leads to superior results.

What you'll learn

After completing this project, you will be able to:
  • Design reflection workflows with LangGraph that enable AI to critique and improve its own outputs
  • Implement generation, evaluation, and refinement stages in a connected agentic system
  • Create feedback mechanisms that allow AI to objectively assess its content quality
  • Build conditional logic to control when reflection cycles should continue or end
  • Apply reflection principles to various AI applications beyond the project examples

Who this project is for

This project is perfect for:
  • Software developers seeking to build more sophisticated and reliable systems
  • Data scientists working with large language models
  • ML engineers interested in self-improving agentic AI applications
  • Content creators looking to leverage AI with refinement capabilities

What you'll need

Before beginning this guided project, you should have:
  • Basic understanding of Python programming
  • Familiarity with LLM concepts and applications
  • Access to a modern web browser for the IBM Skills Network Labs environment

Why enroll

By the end of this project, you'll possess the skills to build AI systems that think twice—generating content and then critically reviewing it before delivering final results. This reflection capability is the key difference between basic AI applications and truly intelligent systems. Whether you're developing content generators, virtual assistants, or analytical tools, incorporating reflection mechanisms with LangGraph dramatically improves output quality and reliability. Join this project to learn how to transform one-shot AI responses into thoughtful, refined content through the power of self-critique and iteration.

Instructors

Kunal Makwana

Data Scientist

I’m a passionate Data Scientist and AI enthusiast, currently working at IBM on innovative projects in Generative AI and machine learning. My journey began with a deep interest in mathematics and coding, which inspired me to explore how data can solve real-world problems. Over the years, I’ve gained hands-on experience in building scalable AI solutions, fine-tuning models, and leveraging cloud technologies to extract meaningful insights from complex datasets.

Read more

Faranak Heidari

Data Scientist at IBM

Detail-oriented data scientist and engineer, with a strong background in GenAI, applied machine learning and data analytics. Experienced in managing complex data to establish business insights and foster data-driven decision-making in complex settings such as healthcare. I implemented LLM, time-series forecasting models and scalable ML pipelines. Enthusiastic about leveraging my skills and passion for technology to drive innovative machine learning solutions in challenging contexts, I enjoy collaborating with multidisciplinary teams to integrate AI into their workflows and sharing my knowledge.

Read more

Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

Read more

Contributors

Karan Goswami

Data Scientist

I am a dedicated Data Scientist and an AI enthusiast, currently working at IBM's Skills Builder Network. Learning how some simple mathematical operations could be used to make predictions and discover patterns sparked my curiosity, leading me to explore the exciting world of AI. Over the years, I’ve gained hands-on experience in building scalable AI solutions, fine-tuning models, and extracting meaningful insights from complex datasets. I'm driven by a desire to apply these skills to solve real-world problems and make a meaningful impact through AI.

Read more

Boyun Leung

UX Designer

Creating and designing delightful experiences.

Read more