Abstract: A system for providing and managing an automated agent. The automated agent may interact with a customer and utilize rules and instructions to determine a response to the customer and actions to perform. The customer interactions by the automated agent are driven by a set and/or subset of instructions that can be analyzed before each automated agent response or action. The subset of instructions, customer conversational input, and other content can be processed by a machine learning model, which may be implemented as a large language model (LLM).

Abstract: A system for generating instructions based on part from human training materials, wherein the generated instructions are used to train automated agents. The training materials may include training manuals, knowledge-base documents, articles, and other content. The training materials are processed and converted to a format that is digestible by a machine learning model. The processed training materials can be submitted as a prompt, along with role information and prompt instruction information. The prompt instructions provided with the role and training materials may direct the machine learning model to find relevant generated instructions from the provided training materials. The machine learning model processes the prompt and outputs extracted instructions from the content. The instruction document may be stored in a vector database for later use.