The ReAct Method: How AI Agents Reason and Act
By Dorian Laurenceau
📅 Last reviewed: April 24, 2026. Updated with April 2026 findings and community feedback.
Standard LLMs can think, but they cannot DO. They cannot search the web, run code, query databases, or send emails. AI Agents change this by combining LLM reasoning with tool use, creating systems that think, act, observe the result, and think again. The ReAct framework (Reasoning + Acting) is the architecture that makes this possible.
Why Agents Matter
Think about how you solve a complex problem. You do not just think about it, you:
- →Reason about what to do next
- →Act by looking something up, running a calculation, or asking someone
- →Observe the result
- →Repeat until the problem is solved
This is exactly what ReAct agents do. They are AI systems that can plan, use tools, learn from feedback, and iterate toward a solution.
The ReAct Loop
ReAct: Reason, Act, Observe
The core loop of every AI agent
Thought
Thought: The user wants to know the current weather in Paris and whether they need an umbrella. I need to check the weather API first.💡 The thought step makes the agent's reasoning explicit and debuggable.
Agents vs Chains: When to Use What
Chains vs Agents
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What makes ReAct agents more capable than pure CoT reasoning?
Tools: The Agent's Hands
Safety and Guardrails
- →Tool whitelisting, Only give agents access to tools they need. A customer support agent does not need file system access.
- →Confirmation for destructive actions, Require human approval before sending emails, making purchases, or deleting data.
- →Iteration limits, Set a maximum number of ReAct loops (typically 5-10) to prevent infinite loops.
- →Output validation, Check the agent's final answer against the observations to detect hallucination.
- →Cost controls, Each loop is an API call. Set budget limits to prevent runaway costs.
Test Your Understanding
ReAct Agents Knowledge Check
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Q1.What does ReAct stand for?
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What's Next
You now understand the ReAct framework. In the next workshop, you will build your own AI agent with tool use and safety guardrails.
- →AI Agent Architecture Patterns, The 5 workflow patterns for building effective agents
Continue to the workshop: Build an AI Agent with Safety to create an autonomous agent.
Module 6 — AI Agents & ReAct
Create autonomous agents that reason and take actions.
Dorian Laurenceau
Full-Stack Developer & Learning DesignerFull-stack web developer and learning designer. I spent 4 years as a freelance full-stack developer and 4 years teaching React, JavaScript, HTML/CSS and WordPress to adult learners. Today I design learning paths in web development and AI, grounded in learning science. I founded learn-prompting.fr to make AI practical and accessible, and built the Bluff app to gamify political transparency.
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FAQ
What will I learn in this AI Agents guide?+
Understand the ReAct framework that powers AI agents. Learn how models combine reasoning with tool use to solve complex, multi-step tasks autonomously.