Adversarial training of artificial intelligence agents

A computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor of a computer system, cause the computer system to perform steps comprising:

• creating an instance of a system artificial intelligence (AI) agent comprising a large language model that has been pre-trained using a set of system constraints and a set of system objectives;
• retrieving training service requests that are associated with a user AI agent of a plurality of user AI agents that are associated with different types, where each user AI agent is a separate large language model that was pre-trained using a set of training user constraints and a set of training user objectives that differ from at least one other user AI agent and in part determine the type of the user AI agent;
• managing rounds of messaging between the user AI agent and the system AI agent to achieve resolutions to the training service requests;
• generating training examples based on the training service requests from the user AI agent, each training example including, for a given service request and corresponding resolution, at least one round of messaging of the rounds of messaging;
• labeling each training example based on a comparison of a resolution of the training example to a metric associated with an online system;
• and
• training the system AI agent using the labeled training examples, wherein the trained system AI agent is used to determine a resolution for a service request that is associated with a user.

A computer system comprising:

• a processor;
• and
• a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by the processor, cause the computer system to perform steps comprising:
• creating an instance of a system artificial intelligence (AI) agent comprising a large language model that has been pre-trained using a set of system constraints and a set of system objectives,
• retrieving training service requests that are associated with a user AI agent of a plurality of user AI agents that are associated with different types, where each user AI agent is a separate large language model that was pre-trained using a set of training user constraints and a set of training user objectives that differ from at least one other user AI agent and in part determine the type of the user AI agent,
• managing rounds of messaging between the user AI agent and the system AI agent to achieve resolutions to the training service requests,
• generating training examples based on the training service requests from the user AI agent, each training example including, for a given service request and corresponding resolution, at least one round of messaging of the rounds of messaging,
• labeling each training example based on a comparison of a resolution of the training example to a metric associated with an online system, and
• training the system AI agent using the labeled training examples, wherein the trained system AI agent is used to determine a resolution for a service request that is associated with a user.