Of course this has been pushed to the side a bit in the rush towards shiny new pure-LLM approaches, but I think that’s more a function of a rapidly growing user base than of lost knowledge; the experts still keep this in mind, either in these terms or in terms of “Ensembles”. A great example is GPT-4, which AFAIU got its huge performance increase mostly through employing a “mixture of experts”, which is clearly a synonym for a society of agents or an ensemble of models.
On the same note, I suggest the following: training a transformer by "slicing" it in group of layers and force it to emit/receive tokens at each of those group's boundaries. What I expect: using text rather than neural activations should lead to decreased performance.
This is something you can observe in our societies: intelligence doesn't compose, you just don't double a group's overall intelligence by doubling the number of members. At best you'll observe decreasing return, at worst intelligence will decrease.