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When you type a message to an AI, it doesn't write the entire reply at once. It generates one small piece of text (a "token") at a time, choosing the next token based on the probabilities it assigns to possible options.

Suppose the model needs to choose its next word. It might assign probabilities like these:

The model then samples from this distribution, like rolling a weighted die. "Search" is the most likely choice, but "calculate" and "email" are also possible.

This sampling process introduces randomness, which is one reason the same question can produce different answers.

What Does Temperature Do?

Temperature controls how sharply or evenly the model distributes probabilities across its possible next tokens.

Technically, it rescales the model's raw scores, called logits, before converting them into probabilities. This changes how likely each token is to be selected.

Let's look at how different temperature settings can affect the same choices.

These values are illustrative. Actual probabilities depend on the model's raw scores and the temperature setting.

• Low temperature: The most likely options become more dominant. The model tends to produce more predictable output.
• High temperature: The probabilities become more evenly distributed, giving less likely options a better chance of being selected. The output can become more varied and surprising.
• Temperature near zero: The model increasingly favors the highest-probability token. At zero, many implementations use greedy decoding, which selects the top token directly.

Think of temperature as a dial that controls how much randomness influences the model's choices.

How Temperature Affects a Chatbot

In a chatbot, you ask a question, the model generates an answer, and you read the result. Temperature influences how that answer is phrased and which ideas it explores.

• Low temperature: Answers tend to be more consistent, focused, and predictable.
• High temperature: Answers can be more varied and creative, but may also become less focused or more prone to errors.

If an answer is slightly off, you can notice it and ask again. A human is usually checking the result. The cost of a poor choice might be an awkward sentence or an irrelevant suggestion.

Now consider what happens when the same model operates as an agent.

How an AI Agent Works

An AI agent typically follows a loop:

1. Observe the current situation.
2. Decide what to do next, such as searching the web, running code, calling an API, or editing a file.
3. Execute the action.
4. Examine the result.
5. Repeat until the task is complete.

A single task might involve 10, 20, or even 100 steps. Some steps may run without a human checking each decision.

The model still chooses tokens, but those tokens can now determine actions that influence everything that follows. This makes temperature an important setting to consider.

Why Temperature Matters More in Agents

1. Small Errors Can Compound

Suppose an agent completes a 20-step task, and each step has a 98% chance of being correct. If we assume the steps are independent and every step must be correct, the probability that all 20 steps succeed is:

0.98 ** 20  # approximately 67%

At 95% reliability per step, the probability falls to about 36%. At 99%, it is about 82%.

This illustrates how reliability can decrease across a long sequence of steps.

Higher temperature does not automatically make every step less reliable. However, when it increases the chance of selecting unsuitable tokens or actions, those mistakes can affect later steps. An incorrect early decision might send the agent down the wrong path, making the rest of the task harder to complete.

In a chatbot, a poor choice might produce an awkward sentence. In an agent, it could derail an entire workflow.

2. Agents Often Produce Structured Output

Chatbots primarily produce text for humans to read. Agents often generate structured outputs that software must interpret, such as tool calls, JSON objects, and API parameters.

For example:

{
  "tool": "search_files",
  "query": "invoice_2026.pdf",
  "max_results": 5
}

A computer processes this output according to strict rules. A misspelled tool name, an invalid parameter, or malformed JSON can cause a call to fail. Depending on the system, an incorrect but valid call could also perform an unintended action.

Structured output often leaves less room for variation than ordinary prose. This makes reliable generation especially important when an agent interacts with tools.

3. Agent Actions Have Real Consequences

A chatbot's response is usually text on a screen. An agent can act on that response by sending an email, deleting a file, running a command, or spending money.

An unsuitable choice is no longer just an odd sentence. It might trigger an action that is difficult to reverse.

Lower temperature can help make token selection more predictable, but it is not a safety mechanism by itself. Agents still need safeguards such as permission limits, input validation, human approval for sensitive actions, and checks before executing destructive operations.

4. Debugging Requires Repeatability

Suppose an agent fails on step 14 of a 30-step task. You want to run it again, reproduce the failure, and identify what went wrong.

At higher temperatures, sampling may send the agent down a different path on each run. The failure might disappear, making it harder to investigate. Lower temperatures can make the agent's behavior more repeatable, which helps when testing changes and comparing results.

However, repeatability also depends on factors such as the model, the tools it uses, the environment, and the state of external systems. Lower temperature alone cannot guarantee identical runs.

Should You Always Set an Agent's Temperature to Zero?

Not necessarily. Although predictable behavior is useful for agents, setting temperature to zero is not a universal solution.

There are situations where variation is valuable:

• Getting stuck in a loop: An agent might repeatedly choose the same failed action. Exploring an alternative approach may help it recover.
• Exploring different solutions: Tasks such as finding information, brainstorming search queries, or generating candidate solutions can benefit from variation.
• Creative tasks within a workflow: An agent writing a draft or proposing ideas may benefit from a moderate temperature, even if other steps require more predictable behavior.

The right setting depends on the task. A workflow can also use different temperatures for different stages, favoring predictable outputs for tool calls and allowing more variation during brainstorming.

Conclusion

Temperature controls how randomness influences a language model's token choices. In a chatbot, this mainly affects the variety and predictability of its responses. In an agent, those choices can influence a chain of tool calls and real-world actions.

That is why temperature deserves more attention in agent workflows. Small differences in individual decisions can affect the outcome of a longer task, while unpredictable behavior can make failures harder to reproduce and debug.

The goal isn't to eliminate randomness everywhere. It's to control where variation is useful and where predictable behavior matters more.

Your team's attention is limited, and the deluge of AI-generated code is making it harder to keep production reliable and secure without slowing you down.