This leaves me two questions 1. Will we ever reach (or have we already) where we can more empirically measure the cost of the adding the nth agent. Like the way businesses of different sizes can know when the nth employee add diminishes returns overall, can we measure the front with agents depending on task scope, resources, and other factors.

2. On the other end, can we see point where we can reduce how many agents are needed down the minimal set? For example many chains have reduced staff down to the minimal number of employees (note I do not agree with this) and still the balance sheet is in the black. I think in the hyper optimization and efficiency society we're in I think this will also happen, although the reality of "I can do 100 agents work with 10" might not be in providers and labs best interest economically.

Great point. It's not a distributed systems problem. It's a problem of many independent components trying to coordinate independently is the load bearing issue.

This was my first article where the AI-isms cancelled my reading attempt.

It's a pretty straightforward problem statement, they didn't really need to outsource the writing.

Interesting.

I wanted to test the veracity of this result so I tested some of my own 100% hand written pieces and what do you know? It came back 100% hand written.

This is a better ad for Pangram than for OP :D

one agent is a special kind of distributed system. It's a distributed system that is undistributed.

Your comment inspired me to read the article.

Try mine writing about a similar thing here written by me, a human man: https://blog.mempko.com/misconception-about-agentic-scaling/

I go and explain why adding more agents can slow you down and the model I use including an example of my own setup.

I’ve got no problem with LLM copywriting but the LLMisms just drip off this thing, to the detriment of its clarity. Shame.