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Chanyoung Park

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Preprint Aug 2026

AutoSaddler: Automatic Harness Optimization with Durable Updates from Agent Execution Traces

The proposed AutoSaddler, an automatic harness optimization framework that formulates harness improvement as an offline learning problem and iteratively updates the harness using failure signals from mini-batches, suggests that automatic harness optimization is a promising path toward more performant and reliable agent systems.

Sungho Park, Wonjoong Kim, Rongyuan Tan et al. · 0 citations