I launched Epho a few weeks ago as an API like this but for all harnesses: https://epho.io
I built it primarily for ourselves: we are building an AI data engineer, and we need a way to run many of them in parallel securely. An API for this seemed like the most obvious path forward. It makes it trivial to bring agentic capabilities into any product surface without having to deal with sandboxes, reliability issues, compatibility problems, and more.
I think it also makes sense from OpenAI's perspective to do this, but also we did find ourselves needing to change models and harnesses quite a bit, which is why I think this needs to be a layer of its own above the labs. It also needs to be a layer above the sandboxes, since many of them are quite brittle.
Overall, I expect a lot of the agent implementations to move in this direction. I think this is a lot saner for engineers to implement and maintain, and it makes it trivial to build agentic stuff into products.
I've commented about this before, I think many LLM based apps nowadays are at risk of being replaced by a product straight from the labs once they prove to be successful.
We've seen this pattern with Apple making their own version of an app that was previously popular on the app store.
The labs are in a perfect position to do this - they have a bunch of data on what's being used and they have direct access to their own models/compute. If an external service is popular, it's relatively trivial for them to estimate how much additional profit they're leaving on the table.
If the usecase isn't far from their core business (and things like this absolutely aren't), with their size, why wouldn't they eat other people's lunches?