# AutoSaddler：自动优化智能体框架并防回退

- 来源：Rohan Paul (@rohanpaul_ai)
- 发布时间：2026-08-27 07:49
- AIHOT 分数：36
- AIHOT 链接：https://aihot.virxact.com/items/cmtary4oc07psroam9i1u50bf
- 原文链接：https://x.com/rohanpaul_ai/status/2092761296819425328

## AI 摘要

AutoSaddler 从失败轨迹中自动重写智能体的提示词、工具与中间件，但只保留能提升留出开发集性能的更新。论文发现，若无泛化校验，自动优化结果反而不如手写框架；建议用补丁未针对的任务集评估净收益，而非仅看修复数。

## 正文

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"