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Build Reasoning and Acting AI Agents with ReAct

IntermediateGuided Project

Create intelligent agents that combine step-by-step reasoning with targeted actions using the ReAct framework. Learn to build AI systems that can break down complex queries, search for information, analyze results, and take appropriate actions to solve problems. This project teaches you to implement the complete ReAct cycle in LangGraph where agents think before they act, observe outcomes, and refine their approach. In 45 minutes, master techniques for developing more reliable and transparent AI assistants capable of multi-step problem solving.

Language

  • English

Topic

  • Artificial Intelligence

Skills You Will Learn

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

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 45 minutes

Platform

  • SkillsNetwork

Last Update

  • July 8, 2025
About this Guided Project
Traditional language models often struggle with complex, multi-step reasoning tasks because they attempt to solve problems in a single pass. This guided project introduces you to ReAct (Reasoning and Acting)—a powerful framework that transforms how AI agents approach problem-solving by combining deliberate thinking with purposeful action.

Through hands-on implementation, you'll build a complete ReAct agent that tackles problems through a structured, iterative process. Unlike conventional approaches, ReAct agents alternate between reasoning about what they know, taking appropriate actions to gather information, observing results, and continuing to reason until they reach a solution. This methodical approach dramatically improves reliability, transparency, and overall performance on complex tasks.

Using LangGraph and LangChain, you'll create a complete ReAct agent architecture with custom tools for web searching, text analysis, and processing. You'll learn to structure prompts that guide the model's reasoning, define state management for tracking progress, and build a workflow graph that implements the full ReAct cycle. By the end of this project, you'll have a functional agent that can handle real-world queries requiring multiple reasoning and action steps.

What you'll learn

After completing this project, you will be able to:
  • Implement the complete ReAct framework using LangGraph and LangChain
  • Design specialized tools for your AI agents to interact with external systems
  • Structure effective reasoning prompts that guide models through complex problem-solving
  • Build graph-based workflows that implement the reasoning-acting-observing cycle
  • Debug and trace agent reasoning processes to improve reliability
  • Apply ReAct patterns to create more capable AI assistants for real-world applications

Who should enroll

This project is perfect for:
  • AI developers looking to create more reliable, reasoning-based agent systems
  • NLP practitioners interested in improving how language models tackle complex tasks
  • Software engineers wanting to implement structured reasoning in their AI applications
  • Technical professionals seeking to understand and apply advanced AI agent architectures

What you'll need

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

Why enroll

The ReAct approach represents a significant advancement in how AI agents solve problems. By completing this project, you'll gain practical experience implementing agents that don't just generate responses, but think through problems step-by-step, take actions to gather information, and refine their understanding before providing answers. This methodology produces dramatically more reliable, transparent, and capable AI assistants across a wide range of applications—from information retrieval and analysis to complex problem-solving and decision-making scenarios. These skills are increasingly valuable as organizations seek to deploy more sophisticated AI solutions that users can trust and understand.

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.

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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.

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Contributors

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.

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Tenzin Migmar

Data Scientist

Hi, I'm Tenzin. I'm a data scientist intern at IBM interested in applying machine learning to solve difficult problems. Prior to joining IBM, I worked as a research assistant on projects exploring perspectivism and personalization within large language models. In my free time, I enjoy recreational programming and learning to cook new recipes.

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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.

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