ReAct Pattern

The ReAct pattern (short for "Reasoning and Acting") is a technique for building AI agents that combines two things that used to be done separately: the model's step-by-step reasoning and the execution of actions in the real world, such as querying a database, calling an API or looking up a piece of data. Instead of answering in a single shot, the agent moves through a think, act and observe loop: it thinks about what it needs ("thought"), does something concrete ("action"), looks at the result ("observation") and thinks again with that new information, until it reaches the final answer.

The idea, popularized by a paper from Google and Princeton researchers in 2022, is that interleaving thought and action produces more reliable agents: reasoning guides which tool to use, and the tools' results correct the reasoning, which reduces errors and fabrications. This loop is at the core of most of today's agent frameworks and of the agentic AI that automates business tasks, part of what AI agents orchestrate across a company's processes.

How the ReAct loop works

An agent that follows the ReAct pattern doesn't produce an answer in one go: it runs through a three-move cycle that repeats until the task is done.

1. Thought (Reasoning): the model reasons in natural language about what it still needs to know and what the next step is. For example: "To answer how much this customer was invoiced in May, I first need to look up their orders."
2. Action (Acting): the agent picks and runs an available tool (a SQL query, an API call, a search in a document). This is what is known as tool calling.
3. Observation: the result of that action (the data the tool returned) is inserted back into the model's context.

With that observation, the agent returns to the thought step and decides whether it can answer now or needs another action. The loop repeats until the model concludes it has everything it needs for the final answer. The key difference from a "plain" model is that ReAct lets it look up information it doesn't have and check its own assumptions before committing to an answer.

Why it matters for a business

The practical value of ReAct is that it turns a language model, which only predicts text, into something capable of operating on real systems in a traceable way. Because each action is logged alongside the thought that triggered it, the team can audit the agent's chain of decisions: see what it queried, what it got back and why it reached that conclusion. That transparency is decisive in B2B settings where a wrong answer has a cost (collections, inventory, customer data).

The pattern also reduces hallucinations: instead of making up a number, the agent is pushed to go get it with a tool. It doesn't eliminate them entirely, but it makes them much less likely because the answer rests on observed data, not just on the model's memory.

A concrete example (Latin America)

Picture a consumer goods company in Argentina with a customer service agent that receives this over WhatsApp: "When will my order 4821 arrive?" A ReAct agent would reason like this:

• Thought: "I need the status of order 4821; it's in the logistics system."
• Action: it queries the tracking API with the order number.
• Observation: the API returns "out for delivery, ETA today 18:00."
• Thought: "I have the data now, I can answer."
• Answer: "Your order 4821 is out for delivery and will arrive today before 6:00 p.m."

Without ReAct, the model could make up a date. With ReAct, the answer comes from real data queried at that moment.

Common mistakes when implementing it

• Infinite loops: if the agent can't find the information, it can keep repeating thought and action without converging. That's why it is given a cap on iterations.
• Poorly described tools: if each tool's description is ambiguous, the model picks the wrong action. The quality of that catalog matters as much as the model.
• Context overflow: each observation adds text to the context; on long tasks the context window fills up and the agent "forgets" earlier steps.

How it differs from other approaches

ReAct is often compared with two close alternatives. This table sums up the differences.

Chain of Thought improves reasoning but can't go out and fetch data; ReAct gives it hands. That's why ReAct is the foundation of agents that actually get work done, not just hold a conversation. Frameworks such as Agentforce and the reasoning engines behind agentic AI implement variants of this loop, sometimes combining it with upfront planning or with several coordinated agents.