At a Glance

Name: Build Reasoning and Acting AI Agents with ReAct
Price: Free CAD
Rating: 5.0 (1 reviews)
Author: joseph_santarcangelo, kunal_makwana, tenzin_migmar, karan_goswami, faranak_heidari

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.

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.

Offered By: IBMSkillsNetwork

Build Reasoning and Acting AI Agents with ReAct

At a Glance

What you'll learn

Who should enroll

What you'll need

Why enroll

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