Applied Compute adds self-distillation workflows to AC2 for production agent feedback
The tooling uses corrections, tool failures and other agent traces to train models on behavior encountered after deployment.
By Ryan Merket · Published
Primary source: Applied Compute on X
Why it matters
Applied Compute is turning agent logs into training infrastructure. If AC2 can control regressions, proprietary production feedback could become a durable advantage for enterprise models.
Applied Compute added native on-policy self-distillation and relevance-masked self-distillation workflows to AC2, giving enterprise AI teams a way to turn production traces and user corrections into model-training data, the company said in an August 4th thread on X.…
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