LLM topic discovery in Virtual Agent

  • Release version: Australia
  • Updated March 12, 2026
  • 2 minutes to read
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    Summary of LLM topic discovery in Virtual Agent

    Large language models (LLMs) enhance Virtual Agent by enabling it to understand and process user statements in natural language more effectively than traditional Natural Language Understanding (NLU) methods. LLMs simplify setup by eliminating the need for extensive intent modeling and keyword mapping, allowing faster creation, configuration, and deployment of conversational topics within Virtual Agent Designer when ServiceNow Otto is enabled.

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    Key Features

    • Topic Discovery: LLMs automatically match user utterances to appropriate topics using plain language descriptions provided by topic authors, removing the need for complex NLU models, intents, or backup keywords. When multiple matches exist, users are presented with a list of options.
    • Entity Extraction: Unlike NLU, LLMs extract relevant entity values directly from user input without requiring explicit mapping or association with input nodes, improving accuracy and reducing configuration effort.
    • Topic Switching: LLMs support seamless, natural language-driven switching between conversation topics within a single session. Users can change requests on the fly, and Virtual Agent dynamically activates the appropriate topic without restarting the conversation. Note that switching is not supported within catalog ordering flows.
    • Small Talk Handling: LLMs can manage unrelated or casual user questions by leveraging small talk topics and filters, enhancing conversational flow and user engagement.

    What This Enables for ServiceNow Customers

    By leveraging LLM topic discovery, ServiceNow customers can accelerate Virtual Agent topic development and improve user experience with more natural, flexible conversations. The reduction in manual NLU modeling tasks leads to faster deployment and easier maintenance. Customers can expect Virtual Agent to better understand user intents, extract necessary data seamlessly, and handle dynamic conversation shifts effectively.

    This capability allows businesses to build more intelligent, responsive virtual agents that reduce training overhead while delivering enhanced service automation and support.

    Large language models (LLMs) enable Virtual Agent to process user statements in plain language. Conversations that use LLMs can outperform those conversations that use Natural Language Understanding (NLU), with easier setup.

    How LLMs work in Virtual Agent

    Watch this video to learn about LLM topics in Virtual Agent Designer.

    When you create a topic in Virtual Agent Designer, you can select LLM as the model type for your topic whenever ServiceNow Otto in Virtual Agent is turned on. Virtual Agent then uses LLM generative AI to discover topics that match the user's intent.

    Unlike NLU topics, LLMs don't require models, intents, or keywords to be linked to the topic. LLMs can discover topics and perform language-related tasks, such as text generation for case summaries and resolution notes, without months of training on NLU models. Overall, you can create, configure, and deploy LLM topics faster than working with NLU.

    With LLMs, Virtual Agent can do the following:

    • Perform topic discovery without needing a singular declared intent in a given topic.
    • Find intents without backup keywords as in NLU modeling.
    • Extract entity values without prior mapping as in NLU modeling.
    • Handle multiple conversation topic switches in a single conversation session.

    For more information about LLMs, see Large language models on the ServiceNow AI Platform®.

    Topic discovery

    With LLM topic discovery, topic authors no longer need to create and maintain complex NLU models and intents with backup keywords. The LLM does all of the heavy lifting for you. The only requirement is a robust, plain language topic description on the Properties tab in Virtual Agent Designer. The LLM uses this description to find the best topic match for the user utterance. If there are multiple potential matches, the user will see a list of topics to choose from.

    For example, if a user asks Virtual Agent to calculate a ride share fee, the LLM finds an existing topic to calculate the ride share with a tip.

    Entity extraction

    With LLM topics, the LLM has all the information needed to determine if an utterance has the information to fulfill a request. Unlike NLU models, you don't need to associate entities with a user input node or add nodeless NLU entities as input variables to a topic. The LLM simply finds the entity that most closely fits the user intent.

    Topic switching

    Topic switching is faster and easier with LLMs compared to NLU topics. The LLM processes your requests to change intent made in natural language, and activates the appropriate topic.

    For example, if you start a conversation by asking for a mobile phone, you don't have to cancel the order first or restart the conversation. Instead, you can ask Virtual Agent to order a laptop instead. Virtual Agent immediately switches from the mobile phone topic to the laptop topic. Topic switching can be done during a query, but not within a catalog ordering flow.

    Another example is when a user might ask a casual question or engage in small talk. The question might be unrelated to the original request. You can create small talk topics or set up small talk filters to help the Virtual Agent match and launch the appropriate conversation for the switched topic. For more information, see Create a small talk topic and Configure small talk filters.

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