Can an Apartment Search Agent Call the Model Fewer Times and Still Find Good Matches?

An apartment-finding AI agent can drastically reduce its operational costs by using a series of filters before ever calling a powerful language model. By first using simple database queries for criteria like city and rent, and then employing a cheaper AI model for initial text analysis, the system avoids expensive processing for most listings. This tiered approach successfully cut costs by over 95% compared to an already optimized baseline, all while finding every correct apartment match in a test of 2,500 comparisons. Surprisingly, the experiment also found that giving the advanced AI less text to analyze could actually improve its accuracy on difficult cases.