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A decision model picks one option from a set you provide. A generative LLM writes text one token at a time. Many parts of an AI system need a decision, not text: which queue, which tool, which agent, which label.

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How Krun One decides

For each question, Krun One reads the context together with every option and scores the options against each other. The scores become a probability distribution over your options. The highest-probability option is the answer, unless the model abstains.
Because Krun One only scores the options you send:
  • The answer is always valid. choice is one of your option ids, or null. You don’t need to parse output, retry malformed JSON or handle labels that don’t exist.
  • Options are defined per request. Change the label set by changing the request. There is nothing to retrain or redeploy.
  • Every answer carries a distribution. You see how likely each option is, not only the winner.
  • The model can decline. When the two best options are too close, it abstains instead of guessing.

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When to use Krun

Krun is a good fit when:
  • the set of possible outcomes is known at request time,
  • you need the same input to be handled consistently,
  • you want to route uncertain cases to a fallback, such as a human, an LLM or a default flow.
Common examples are intent routing, tool routing, agent routing, ticket triage and content classification.

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When to use an LLM instead

Use a generative model when the output is open-ended: writing a reply, extracting free-form fields, summarizing, or reasoning over many steps. Krun and an LLM work well together: Krun decides where a request goes, and the LLM does the open-ended work once it gets there.