@rohanpaul_ai: Automatically patching an agent's harness is easy; keeping the patches that help is the hard part. So if you let a mode…
Summary
AutoSaddler is a method for automatically patching agent harnesses from execution traces, ensuring updates generalize by testing on a held-out set to prevent regressions.
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Automatically patching an agent’s harness is easy; keeping the patches that help is the hard part.
So if you let a model rewrite your prompts, tools, and hooks from failure traces, spend the rollout budget on a held-out split that catches collateral damage.
Harness optimization does work, but this paper finds that without a check on whether an update generalizes, the result lands below the hand-written harness it started from.
AutoSaddler diagnoses failed traces from a mini-batch, treats the harness as code, patches prompts, tools, and middleware, and keeps only updates that also improve a held-out development set.
If you’re tuning an agent by hand or with an LLM in the loop, hold out a set of tasks that the patch was not written for, and score fixes minus regressions rather than fixes alone.
– arxiv. org/abs/2608.23041
Title: “AutoSaddler: Automatic Harness Optimization with Durable Updates from Agent Execution Traces”
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