Introduction to Reasoning › The Problem with Direct Answers

Let's start with a simple example. Ask an AI agent this question:

A farmer has 17 sheep. All but 9 die. How many sheep are left?

If the agent jumps straight to an answer, it might say "8 sheep" by subtracting 9 from 17. But that's wrong. The correct answer is 9, because "all but 9 die" means 9 survive.

The agent made a common mistake: it rushed to calculate without carefully reading the question. This happens because language models are trained to predict the next words, and sometimes the most likely next words aren't the most thoughtful ones.

Now try asking the same question differently:

A farmer has 17 sheep. All but 9 die. How many sheep are left?

Let's think through this step by step.

With that simple addition, the agent is more likely to respond:

Let me work through this carefully:

1. The farmer starts with 17 sheep
2. The phrase "all but 9 die" means that 9 sheep survive
3. If 9 sheep survive, then 9 sheep are left

The answer is 9 sheep.

Much better. By prompting the agent to think step by step, we helped it avoid the trap and arrive at the correct answer. This is the core idea behind reasoning: giving the model space to work through problems methodically.