Using artificial intelligence agents for servicing search queries

A non-transitory computer-readable storage medium storing instructions that, when executed, cause a processor to perform steps comprising:

• receiving, from a client device, a search query entered at an item search interface presented at the client device;
• retrieving one or more items that are responsive to the search query;
• selecting a set of complementary items to the one or more items that are responsive to the search query;
• applying a first generative language model to each of the set of complementary items, wherein the first generative language model is tuned on presentation requirements for each of the set of complementary items, wherein the presentation requirements associated with each complementary item include a prospective number of presentation instances for the respective complementary item at one or more client devices;
• receiving, from the first generative language model, a first set of scores for the complementary items;
• applying a second generative language model to the set of complementary items, wherein the second generative language model is tuned with historical presentation instances of each of the set of complementary items;
• receiving, from the second generative language model, a second set of scores for the complementary items;
• aggregating, for each of the set of complementary items, respective scores from the first set of scores and the second set of scores;
• ranking the set of complementary items based on the aggregate scores;
• and
• sending, to the client device, the ranked set of complementary items and the one or more items for display at the item search interface presented at the client device.

A system comprising:

• a processor;
• and
• a non-transitory computer-readable storage medium storing instructions that, when executed, cause the processor to perform steps comprising:
• receiving, from a client device, a search query entered at an item search interface presented at the client device;
• retrieving one or more items that are responsive to the search query;
• selecting a set of complementary items to the one or more items that are responsive to the search query;
• applying a first generative language model to each of the set of complementary items, wherein the first generative language model is tuned on presentation requirements for each of the set of complementary items, wherein the presentation requirements associated with each complementary item include a prospective number of presentation instances for the respective complementary item at one or more client devices;
• receiving, from the first generative language model, a first set of scores for the complementary items;
• applying a second generative language model to the set of complementary items, wherein the second generative language model is tuned with historical presentation instances of each of the set of complementary items;
• receiving, from the second generative language model, a second set of scores for the complementary items;
• aggregating, for each of the set of complementary items, respective scores from the first set of scores and the second set of scores;
• ranking the set of complementary items based on the aggregate scores;
• and
• sending, to the client device, the ranked set of complementary items and the one or more items for display at the item search interface presented at the client device.