NLU intents

  • Release version: Zurich
  • Updated July 31, 2025
  • 3 minutes to read
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    Summary of NLU intents

    NLU intents are fundamental to enabling ServiceNow models, such as Virtual Agent and Search, to accurately interpret and respond to user inputs by matching them with system actions. Intents translate natural language utterances into actionable tasks, driving effective user interactions. Proper coordination of intents, utterances, entities, and vocabulary is critical to enhance the accuracy of intent prediction and overall model performance.

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    Key Features

    • Intent Management: You can create and manage up to 750 intents per model, with a practical recommendation to keep models under 300 intents or 4,500 utterances for efficient training and publishing times.
    • Utterances: Each intent requires at least 5 training utterances (examples of user inputs) to effectively train the model. Utterances are limited to 25 words or 200 characters and a model can contain up to 20,000 utterances. Utterances can be edited, copied, moved, or deleted to refine intent accuracy.
    • Vocabulary Integration: Adding relevant organizational or domain-specific vocabulary using the @ symbol helps improve the system’s understanding and intent prediction accuracy.
    • Entities: Entities add context to utterances, enriching the model’s ability to interpret user inputs and execute precise actions.
    • Intent Activation: Intents can be enabled or disabled to control their use in predictions, but intents linked to published Virtual Agent topics cannot be deactivated or deleted without impacting the linked topic.
    • Issue Detection and Resolution: The system identifies conflicts or overlaps between intents, helping you maintain clean, effective intent sets and improve model reliability.

    Practical Guidance for Customers

    • Start by creating at least five intents before conducting thorough testing to reduce interference between intents.
    • Use realistic and diverse utterance examples to enhance model accuracy.
    • Include organizational vocabulary to tailor responses to your users’ language patterns.
    • Regularly review and resolve intent conflicts to ensure precise intent prediction and system actions.
    • Retrain your model after enabling or disabling intents to update its predictive capabilities.

    Expected Outcomes

    By effectively leveraging intents, utterances, vocabulary, and entities, you enable your ServiceNow NLU models to accurately comprehend user language and trigger appropriate system actions. This results in more responsive Virtual Agent interactions and improved search relevance, ultimately enhancing user experience and operational efficiency.

    Intents drive your models' responses by matching a system action to user inputs. Models with good intents help Virtual Agent and Search respond to your users accurately.

    Think of intents as the core that drives the comprehension of natural language. Intents help the models translate utterances from your users into actions that the system can perform. Intents perform best if your utterances, entities, and vocabulary work together to support your model. Using vocabulary and realistic utterance examples can help the system to be more accurate when predicting intents.

    When creating your Natural Language Understanding (NLU) model, you add intents to match user inputs with system actions. The more intents that a model has, the more actions it can take when it receives utterances from your users. Model intents can perform various actions, from creating hardware service requests to adding users to groups.

    To access your model's intents, navigate to a model's overview page. In the Build and train your model phase, select View phase. The Intents tab shows by default.

    Intents tab of the Build and train your model phase

    A model can contain up to 750 intents. However, models with over 300 intents or 4500 utterances, whichever comes first, take longer to train, test, and publish.

    Note:
    As you create intents, keep in mind that they can sometimes impact each other. For example, you build and test an intent that works on one or two utterances. But when you test it in a larger intent environment, it might behave differently. To reduce the likelihood of such an event, you may want to create at least five intents in a model before you start proper testing.

    To start adding content to your model, see Create an NLU intent.

    The Enabled column shows whether or not the intent is active in predictions. An NLU admin can deactivate an individual intent but keep it in the model.

    However, if an intent is mapped to a published Virtual Agent topic:

    • You cannot deactivate the intent.
    • You cannot delete the intent.
    After changing the Enabled status of an intent, retrain the model.

    Utterances

    Intents contain training utterances, which are examples of inputs that the model may see from your users. Each intent in a model has its own utterances. When trained, the model learns to recognize similar utterances from your users and then respond with the matching intent.

    Select an intent to access the intent details page. The Utterances tab lists all the utterances currently in the intent.

    Utterances tab for an example intent.

    After adding utterances, you can edit, copy, move, or delete the utterances using the icons in the right column. You can move or delete multiple utterances by selecting the box on the left first and using the Perform action on selected rows button.

    Here are some things to consider when adding utterances to intents:
    • A model must have at least 1 intent, with a minimum of 5 utterances in each intent.
    • An intent needs at least 5 utterances to begin training.
    • The system currently supports utterances up to 25 words or 200 characters. Utterances that exceed that limit fail to return an intent prediction.
    • The system currently supports up to 20,000 utterances in a single model.
    • Models with more than 4,500 utterances take longer to train, test, and publish.

    Provide vocabulary for any words or phrases that are relevant to your organization or domain when adding utterances to your model. The vocabulary helps with intent prediction for words and phrases that your users are likely to use. You can use the @ symbol when adding an utterance to call on a vocabulary source. For more context and examples, see NLU vocabulary.

    Associated entities

    Your model uses entities to provide additional context and meaning when predicting user input. You add entities to the training utterances of your intent to provide the system with more information to perform the intended action.

    The Associated entities tab for an example intent.

    For more information, see NLU entities.

    Intent issues

    Building large models increases the chance that intents overlap, conflict, or fail to contain enough training utterances. For example, the utterance examples in one intent may end up identical to the examples in another intent. If your intents conflict, the model may not know which intent to predict when receiving user input.

    If your model has issues or conflicts, the Intents page displays cards showing the number of intents affected. In the Intents tab of the Build and train your model phase, the issue cards are highlighted.

    Select a card to see a filtered list of intents with that issue. Resolving issues ensures that your intents meet the requirements and work as intended.

    For more information, see Resolve intent issues.