ReAct Pattern (Reason + Act)

The Technical Definition

ReAct stands for Reason + Act. It’s the agent design pattern, introduced by Yao et al. in 2022, where a language model alternates between two kinds of output: a reasoning step (“I should look up the customer’s account first”) and an action step (calling a tool — a database query, an API, a search). The model sees the result of the action, reasons again, takes another action, and continues until it decides the task is done.

The loop looks like this: thought, action, observation, thought, action, observation, until a final answer. Almost every “AI agent” you’ve heard of in 2026 — Claude’s tool use, OpenAI’s function calling, every framework from LangGraph to CrewAI — is some implementation of this pattern. It is the default architecture.

What This Actually Means for Your Business

When a vendor demos an agent doing something impressive — booking a flight, processing an invoice, resolving a support ticket — what you’re watching is a ReAct loop. The model reads the task, decides what tool to call, calls it, reads the result, decides what to do next.

The pattern works well when three conditions hold: the tools are reliable and return clean data, the task can be decomposed into clear sub-steps, and there’s a recognizable end state. Refund processing, structured data lookups, multi-system reconciliation — these fit. The agent reasons its way through, calls four or five tools, lands the answer.

The pattern breaks in three predictable ways. The first is the infinite loop: the agent reasons, takes an action, gets a result it doesn’t know how to interpret, reasons again about the same thing, takes the same action, and keeps going until it hits a step limit or burns through your token budget. The second is silent failure: the agent declares success but the actual outcome is wrong because one of the intermediate tool results was bad and the model rationalized past it. The third is the wrong tool: the agent picks a plausible-looking tool that’s not actually appropriate, and the reasoning trace makes the mistake look defensible.

The reason these failures matter: in a non-agentic system, a bad input produces a bad output and you catch it. In a ReAct loop, a bad intermediate step produces a confident-looking trace of reasoning that hides the failure. Your debugging tools have to work on the trace, not just the final answer.

Reality Check

What the vendor says: “Our agent reasons through the problem just like a human analyst.”

What that means in practice: It generates text that resembles reasoning, calls tools based on that text, and stops when it generates text that resembles a conclusion. It is not actually checking its work. If the tools return junk, the “reasoning” will accommodate the junk. You need observability on every step in the loop, not just the final answer.

What Operators Actually Do

The teams running ReAct agents in production instrument the loop heavily. Every thought, every tool call, every observation gets logged with a trace ID. They set hard limits on loop depth — usually five to fifteen steps depending on the task — and treat any task that hits the limit as a failure to investigate, not a result to ship. They run evaluation suites that check intermediate steps, not just outcomes, because an agent that gets the right answer through the wrong reasoning is one input change away from getting the wrong answer the same way.

Smart teams also pre-decide which tools an agent can call for which task type. Generic “give the agent every tool” deployments fail more often than scoped deployments. A refund agent gets refund tools. A research agent gets research tools. Cross-task tool access is where the wrong-tool failures happen.

The other pattern that works: human approval gates inserted at specific decision points, not at the end. Letting an agent reason through a six-step task and then asking a human to rubber-stamp the final action defeats the point. Letting the agent propose a step, getting approval, executing, and looping is slower but actually catches mistakes.

The Questions to Ask

1. What does the trace look like when the agent is wrong? Can your team read the reasoning, action, and observation at every step? If the trace is opaque or aggregated, you can’t debug failures.

2. What’s the loop limit, and what happens when it hits? A ReAct agent with no step ceiling will eventually run forever on edge cases. What stops it, and where do those failed runs go for review?

3. Which tools is the agent allowed to call for this task? “All of them” is the wrong answer. Scoped tool access is the difference between an agent that fails predictably and one that fails creatively.