# StarHarness：面向企业环境的智能体框架分层搜索进化方法

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-08-25 08:00
- AIHOT 分数：53
- AIHOT 链接：https://aihot.virxact.com/items/cmtgst2oo082srochy3g6mxm0
- 原文链接：https://arxiv.org/abs/2608.24804

## AI 摘要

StarHarness 提出一种在保持模型权重不变的前提下，为特定环境进化智能体框架（harness）的方法，可优化提示词、工具接口、技能、MCP 提供商及子智能体结构等配置。

## 正文

We present StarHarness, a framework for evolving environment-specific agent harnesses while keeping model weights fixed. The evolved harness can include prompt and task framing, tool interfaces, skills, MCP-backed providers, subagent structure, and agent-loop configuration. StarHarness constructs a compact evolution pool by stratifying tasks according to baseline failure behavior, separates proposer-visible search tasks from proposer-hidden selection tasks, and reserves held-out tasks for evaluating generalization. Across ITBench SRE, EnterpriseOps-Gym ITSM, and AutomationBench Finance, harness evolution improves full-benchmark performance by 20-35 percentage points over the default harness after 4-12 accepted changes per environment. These gains persist on tasks excluded from evolution and transfer without re-evolution across GPT and Qwen model families. Trace analysis links the improvements to interface repairs, environment conventions, and operational knowledge that compresses search, with fewer false-positive diagnoses and shorter trajectories in several settings. StarHarness therefore offers a practical way to reduce persistent model-environment mismatch in tool-rich enterprise tasks.