做智能体开发的团队终于有了让技能自动优化的实用框架——SkillOpt不仅提升了20%的质量，还提供了测试和自进化机制，建议直接集成到你的智能体编排器中试试。
微软的SkillOpt论文提出了一种让AI智能体技能自我进化的方法。一位开发者将其集成到自己的智能体编排器中，发现所有技能都获得了有效的测试框架和自进化能力。在论文图表提取技能上，应用SkillOpt后质量从0.73提升到0.93，提高了20分。作者认为这是当前智能体自我改进能力的明确例证，并指出该思路可扩展到优化智能体模式、工具使用、上下文工程等多个领域。
This SkillOpt paper from Microsoft is a must-read! (bookmark it) I was a bit skeptical of the resu...
This SkillOpt paper from Microsoft is a must-read! (bookmark it) I was a bit skeptical of the results reported in the paper when I shared it a few days ago. However, I managed to integrate it into my agent orchestrator and ran a few experiments. The results are mindblowing. Essentially, all my agent skills now have a proper testing framework and a way to self-evolve. I have started to improve all my agent skills with this. One exciting result was when I applied it to my paper-figure-extraction skill, which requires an agent to do multimodal analysis. In particular, it improved quality by +20 points (0.73 → 0.93). I went to see the extracted tables and figures, and I was absolutely stunned by how much better my skill got at the task. Self-improving AI is in the early days, but I think this work is a clear example of the current ability of agents to self-improve. In this case, it was skills, but it's not hard to imagine how this scales to optimizing agent patterns, tool use, context engineering efforts, agentic search, workflows, evals, and even the harness itself. I already started with a few of these ideas inspired by SkillOpt. Stay tuned! 💬 10 🔄 7 ❤️ 38 👀 3099 📊 25 ⚡