做长周期智能体开发的团队终于不用手动维护脚手架了——Self-Harness让系统自己优化提示和工具流，运行越久越强，建议点开论文看看具体实现。
一项新研究提出“Self-Harness”概念，让智能体脚手架（包括提示、工具和控制流）能够像技能一样从自身运行中学习并改进，而非固定不变。传统脚手架一旦构建便冻结，需要手动维护，而Self-Harness将其变为可学习的工件，每次运行都能自我优化。论文认为，对于长周期智能体，这种自修改脚手架能将维护工作自动化，使系统性能持续累积。该工作由dair.ai团队发布，论文已在arXiv上公开。
// Self-Harness: Harnesses That Improve Themselves // (bookmark this one) Most of the agent scaffo...
// Self-Harness: Harnesses That Improve Themselves // (bookmark this one) Most of the agent scaffolds we rely on today are built once and remain frozen or mostly unchanged. The harness, like the skills, needs to evolve with new models. What if the scaffold rewrites itself? This new work treats the harness, the prompts, tools, and control flow around the model as a learnable artifact that improves from its own runs rather than staying a fixed wrapper you hand-maintain. The scaffolding becomes the part that compounds, run after run. If you run long-horizon agents, a self-modifying harness turns scaffold upkeep from manual work into something the system earns on its own. Paper: arxiv.org/abs/2606.09498 Learn to build effective AI agents in our academy: academy.dair.ai 💬 7 🔄 1 ❤️ 19 👀 1088 📊 13 ⚡