Decision models have changed how we’re thinking about the architecture of our agent harnesses. I saw enough potential in this use case that I launched Mouse-1 last week, a fast decision model optimized for agent harnesses.
I’ve since added fine-tuning and calibration through the API. You can send decisions from your own harness with the correct answers and tune the model for your workflow. The new Outcomes endpoint lets you save decisions and attach the correct answers as you learn them, then use those examples to tune and evaluate against the base model.
Calibration uses your examples to find a confidence cutoff for your workload, so your harness can flag uncertain decisions for review.
We don’t store your decisions unless you turn on Outcomes. It’s there to make collecting examples and tuning the model to your harness easier.
I’d like to hear what decisions you’re making in your agent harness and whether this would be useful for your use case.
Author here.
Decision models have changed how we’re thinking about the architecture of our agent harnesses. I saw enough potential in this use case that I launched Mouse-1 last week, a fast decision model optimized for agent harnesses.
I’ve since added fine-tuning and calibration through the API. You can send decisions from your own harness with the correct answers and tune the model for your workflow. The new Outcomes endpoint lets you save decisions and attach the correct answers as you learn them, then use those examples to tune and evaluate against the base model.
Calibration uses your examples to find a confidence cutoff for your workload, so your harness can flag uncertain decisions for review.
We don’t store your decisions unless you turn on Outcomes. It’s there to make collecting examples and tuning the model to your harness easier.
I’d like to hear what decisions you’re making in your agent harness and whether this would be useful for your use case.
You can try Mouse-1 here: https://mouse.dev/api
?