The claim that a small, fast, and decently accurate model makes a good foundation for agentic workloads seems like a reasonable claim.
However, is cost the biggest limiting factor for agent adoption at this point? I would suspect that the much harder part is just creating an agent that yields meaningful results.
No, I really don't think cost is the limiting factor- it's tooling and competent workforce to implement it. Every company of any substantial size, or near enough, is trying to implement and hire for those roles, and the # of people familiar with the specific tooling + lack of maturity in tooling increasing the learning curve, these are the bottlenecks.
This has been my major concern, so much do that I'm going to be launching a tool to handle this specific task: agent conception and testing. There is so little visibility in the tools I've used that debug is just a game of whackamole.
Did you see this HN submission? https://news.ycombinator.com/item?id=46242838
It seems similar to what you're describing.
I did not. Thanks for the heads up!