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Build a Simple ReAct Agent from Scratch

IntermediateGuided Project

Learn to build intelligent AI agents using the ReAct framework. Develop reasoning systems with tool integration and adaptive behavior for complex problem solving. This guided project covers structured thinking, external tool use, and context management. Ideal for AI/ML engineers, developers, and technical PMs seeking to move beyond static chatbots. Gain hands-on experience in creating agents that reason, plan, and act across tasks—equipping you with practical skills in agent architecture and next-gen AI design.

Language

  • English

Topic

  • Artificial Intelligence

Skills You Will Learn

  • LLM, AI Agent, LangGraph, AI

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 90 minutes

Platform

  • SkillsNetwork

Last Update

  • July 9, 2025
About this Guided Project
With the rise of sophisticated AI applications and the growing demand for intelligent automation, we now have powerful frameworks like ReAct that enable us to build AI agents that truly think and act. This guided project explores how to use the ReAct (Reasoning + Acting) framework to create intelligent agents that solve complex problems through structured thinking, tool usage, and adaptive learning. Instead of building static chatbots that only respond to queries, we'll construct dynamic AI systems that reason through problems step-by-step, gather information using external tools, and adapt their approach based on intermediate results—enabling sophisticated problem-solving that mirrors human cognitive processes.

What You'll Learn 

By the end of this project, you will be able to:
  • Build intelligent AI agents using ReAct: Learn how to implement the Reasoning + Acting framework to create agents that think through problems systematically, use external tools like search engines and calculators, and adapt their approach based on results.
  • Design robust tool integration systems: Master the art of creating and integrating custom tools that extend your agent's capabilities, from web search and mathematical calculations to domain-specific functions tailored to your use case.
  • Implement stateful conversation management: Understand how to maintain context across multi-step reasoning processes, ensuring your agent can build upon previous interactions and maintain coherent problem-solving sessions.
  • Create transparent reasoning systems: Build AI agents that explain their thinking process, making their decision-making transparent and trustworthy for users who need to understand how solutions are reached.

Who Should Enroll

  • AI/ML Engineers with 1-3 years of experience looking to advance beyond basic model deployment into sophisticated agent architecture. This project will teach you how to build production-ready intelligent systems that can handle complex, multi-step problems in real-world applications.
  • Software Developers transitioning into AI who want to understand how to build intelligent systems that go beyond simple API calls. You'll learn to architect agents that can reason, plan, and execute complex workflows autonomously.
  • Technical Product Managers who need to understand the capabilities and limitations of intelligent agents. This hands-on experience will give you the technical depth to make informed decisions about AI system design and implementation.
  • Research Scientists working on AI applications who want to implement state-of-the-art reasoning frameworks in their projects. The ReAct approach represents cutting-edge research in AI agent design, applied to practical problems.

Why Enroll 

This project bridges the gap between theoretical AI knowledge and practical intelligent system development, giving you hands-on experience with one of the most powerful frameworks for building reasoning agents. You'll learn to create AI systems that don't just process information but actively solve problems, gathering data, reasoning through complex scenarios, and taking meaningful actions. The skills you develop—agent architecture, tool integration, state management, and reasoning system design—are foundational to the next generation of AI applications and highly sought after in the industry. By the end, you'll have both a working intelligent agent and the architectural knowledge to build sophisticated AI systems that can tackle real-world challenges with human-like reasoning capabilities.

What You'll Need 

To follow along with this guided project, you should have a solid understanding of Python programming and familiarity with AI/ML concepts. Experience with language models and APIs will be helpful, but not required. All necessary dependencies and tools are provided in the development environment. The platform works best with current versions of Chrome, Edge, Firefox, or Safari.

Instructors

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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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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