LangChain创始人亲自拆解智能体学习系统的关键设计思路，告诉你为什么要自己掌握上下文、租用模型，还透露了LangChain的新动作。
LangChain创始人Harrison Chase回应Jeff Huber的文章，强调追踪(traces)是系统学习的核心，需要通过观测、反馈和更新来持续改进。他认同“拥有上下文租用智能”的观点，认为企业应拥有专有知识并灵活替换模型，同时指出通用智能（模型）与公司专有智能的区分。他提到LangChain正聚焦于可观测性、部署和沙盒等底层基础设施，并暗示有相关新功能即将推出。
great post by jeff! a few thoughts I had as I read along, wrt what we're building at langchain: > d...
great post by jeff! a few thoughts I had as I read along, wrt what we're building at langchain: > design a compound learning machine as you use or deploy agents, they need to be learning! high level, i believe traces are the core of this - you need to see what happened, get feedback on it, and then update the system > own your context, rent your intelligence agree with this sentiment - own your "tacit and institutional knowledge", make it easy to swap out models. i think the phrasing will get confusing tho - what exactly is intelligence? @satyanadella had a great post here ( x.com/satyanadella/s… ) where he says a lot of things that agrees with this, but he also says stuff like "In consuming intelligence, you are creating intelligence... This is your particular intelligence". the nuance is there is general intelligence (eg the models, which you should rent) and then your company proprietary intelligence, which you should own. both could reasonably be called intelligence though, so its confusing > build your tools, buy infra buy the lowest common denominator infra, build specific things on top of it. at LC we try to focus on lowest common denominator infra (observability, deployment, sandboxes), but we can do better - we have some cool stuff coming in that regard > develop rubrics evals are important! > store and learn from production traces yup. was this article just an ad for langsmith all along? who knows! Jeff Huber @jeffreyhuber x.com/i/article/2079… 🔗 View Quoted Tweet 💬 7 🔄 1 ❤️ 6 👀 1295 📊 6 ⚡