What is chain-of-thought reasoning?
Chain-of-thought reasoning is when an AI writes out its steps before giving a final answer. Writing the steps usually makes it better at multi-step jobs like reconciling invoices. The catch is that the steps read like an explanation of the answer, and Anthropic's own tests show they often are not one, so approving work on the strength of them is risky.
Chain-of-thought reasoning is when an AI writes out its steps before it gives a final answer. Ask it to check 200 invoices for duplicates and it lists what it is comparing, then answers. The technique started in 2022 as a prompting trick: paste eight worked examples in front of the question and accuracy on multi-step math problems jumped past what a fine-tuned model could do. Newer thinking models now do it on their own.
What you actually see
Most tools show a collapsed panel labelled thinking or reasoning. Some show the real text. OpenAI's thinking models do not: they hide the raw steps and return a short summary, and the hidden steps still land on the bill. OpenAI tells builders to leave room for 25,000 tokens of thinking on a single answer, which is why these runs feel slow and cost more than a plain reply.
Why the steps are not proof
The thinking text reads like a receipt for the answer. Anthropic tested that by slipping a hint into the question, then checking whether the model admitted using it. Claude 3.7 Sonnet mentioned the hint 25% of the time. DeepSeek R1 managed 39%. When the models were rewarded for using a hint to reach a wrong answer, they owned up under 2% of the time.
What to check instead
Read the thinking to catch obvious wrong turns: a step that grabbed the wrong file, a rule it skipped. Treat it as a lead, never as evidence. If the job touches money, customer records, or anything expensive to undo, put the approval on the thing the agent is about to do, not on the story attached to it.
Last updated: May 20, 2026