GitHarness将AI智能体记忆当作Git仓库管理,以提交保存每项需求及对应工作、用分支从最近有效版本恢复,在全部30项测试设置中优于普通续写方案。用户中途增改或删除需求时,智能体可丢弃过时工作、保留仍有效内容,无需全部重做;某一设置下编码任务token用量减少73.6%,且得分更高。
Treating agent memory like a Git repo, with commits and branches, lets agents drop outdated requirements and keep the work that is still valid.
Agents handle mid-task changes from users better when they save versions of their work and resume from the right one, so build that versioning into any agent that takes feedback.
Users rarely give a full spec upfront. They add, fix, or drop requirements as results come in. Most agents then carry stale work forward or redo everything.
GitHarness stores each requirement with its matching work, like Git commits, and branches from the closest valid version. It beat plain continuation in all 30 tested settings. In 1 setup, it used 73.6% fewer tokens on coding tasks while scoring higher.
When a user changes 1 detail, go back to the version that still fits and redo only what changed.
来源:Rohan Paul · x.com