Airlines dynamically adjust their responses to seat availability queries as they estimate demand for flights. The simplest case is for the most expensive fares. For example, months before a flight leaves all (first class) F seats will be available but as seats are reserved the counts slowly drops until the plane is full; similarly for the most expensive coach class booking code, Y.
But cheaper coach class booking codes like H have response profiles that reflect demand as well as capacity. Suppose the airline sees very high demand for this flight relative to similar flights in the past. They may decide to stop selling cheaper seats so as to force passengers to pay more, or viewed another way, so as to save seats for those who would pay more. Some cheap booking codes might not normally be available at all, and might only be enabled in very low demand situations. Importantly, the information the airline uses to estimate demand changes constantly, so seat availability responses may fluctuate up and down even in absence of any reservations.
One of the biggest problems for the airline is predicting demand. They devote huge efforts to “cleaning” historical data for use in training statistical demand models. Imagine trying to accurately predict demand immediately after a strike, or a plane crash, or for flights to the city hosting the Olympics.