Abstract: Embodiments described herein are directed to training a large action model (LAM) simulator framework. The LAM framework receives a content dataset associated with a task. This dataset includes the task name and at least one user command parameter. The LAM simulator framework identifies an abstract task based on the task name and determines the available tools for the task. It then generates a user command for an artificial intelligence agent, instructing it to complete the task using the abstract task and user command parameters. The AI agent is trained to execute the task over multiple iterations. An iteration involves creating a conversation data object from the user command, available tools, and prior conversation history, generating an action plan using a generative language model and the conversation data object, executing the actions in the plan using the environment and tools, and evaluating the actions.