Chain-of-thought prompting — asking the model to reason through a problem step by step before producing an answer — reliably improves performance on complex tasks. This is well established. What's less discussed is that the reasoning trace it produces is also one of the most useful debugging artifacts in your agentic system.

When an agent produces a wrong answer without a reasoning trace, you have an input and an output and a gap you can't see into. You can change the prompt and see if the output changes, but you're working blind. When an agent produces a wrong answer with a reasoning trace, you can often see exactly where it went wrong — the step where it made a flawed assumption, the point where it misread the context, the place where two constraints conflicted and it resolved them the wrong way. That's actionable information.

This reframes how you should think about chain-of-thought in production systems. It's not just a performance feature to turn on for hard problems — it's observability infrastructure. The reasoning trace is a log of the agent's decision process. Like any good log, it's most valuable when things go wrong.

The practical implication is to preserve reasoning traces even when you don't need them for the task itself. Route them to your logging system. Include them in your eval outputs. When you're investigating a failure, start with the trace. You'll often find the problem faster than any amount of prompt tweaking would reveal it.

There's a caveat worth holding onto: the reasoning trace is an output, not a window into computation. It can be coherent and wrong. A plausible-looking reasoning chain that leads to an incorrect conclusion is still a useful debugging artifact — it tells you the model constructed a believable path to the wrong place, which narrows down what kind of prompt change might help. But don't make the mistake of trusting the trace as proof of correctness.

Think of chain-of-thought as a flight recorder. You hope you never need it. You're glad it was running when you do.