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An LLM Action allows your agent to perform a specialized reasoning task using a separate prompt and model configuration.

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When to use LLM Actions

Use LLM Actions for atomic tasks that require reasoning but don’t necessarily need a full, stateful subagent.
  • Summarization: Condensing a long session history or a document.
  • Translation: Converting user input or agent output into a different language.
  • Formatting: Transforming unstructured text into a specific JSON schema or markdown format.

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Configuration

You can define a unique system prompt, select a specific model (e.g., a smaller, faster model for simple tasks), and set hyperparameters specifically for this action. Like all actions, LLM Actions have an Invocation Mode: Foreground output can stream back as part of the agent’s response, while Background output only enriches session context and is never shown to the user.
LLM Actions run through your Application like any other intent — each action shows up in request monitoring and can be independently fine-tuned as you collect traffic.