At a Glance

Name: Make Your AI Agents Smarter with Reflection in LangGraph
Price: Free CAD
Rating: 0.0 (0 reviews)
Author: karan_goswami, kunal_makwana, faranak_heidari, joseph_santarcangelo, boyun_leung

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.

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.

Offered By: IBMSkillsNetwork

Make Your AI Agents Smarter with Reflection in LangGraph

At a Glance

What you'll learn

Who this project is for

What you'll need

Why enroll