LangChain 创始人 Harrison Chase 谈智能体构建：先优化上下文再动模型
💡 Excellent recap of @hwchase17 + @HarryStebbings' conversation on @twentyminutevc ⤵️
LangChain 创始人聊智能体怎么搭：先优化上下文，再动 harness，还讲了他们怎么省下 coding agent 开销，全是实操经验。
LangChain 的 Harrison Chase 在 Twenty Minute VC 播客中提出智能体公式：模型 + 上下文 + harness，三者任一都可优化。他认为最高 ROI 的做法是先优化上下文，因为领域知识主要存放在这一层。持续学习的闭环是运行智能体、收集 traces、提炼改进、度量效果、再循环，改进可落在任何一层。降低成本上，LangChain 的做法是先加支出可见性、再加控制手段、最后才做优化，他们用这套流程压低了自家 coding agent 的开销。
💡 Excellent recap of @hwchase17 + @HarryStebbings' conversation on @twentyminutevc ⤵️
💡 Excellent recap of @hwchase17 + @HarryStebbings ' conversation on @twentyminutevc ⤵️ Sydney Runkle @sydneyrunkle "it's underrated how hard it is to build agents!" great convo w/ @hwchase17 and @HarryStebbings on @twentyminutevc an agent = model + context + harness, and you can improve an agent by tweaking any of the three. a few takeaways: 1. the highest roi thing you can do is start by optimizing context (then the harness, then the weights); this is where domain knowledge lives and that's incredibly valuable 2. the story of continual learning is: run an agent, collect traces, aggregate learnings, fix something, measure the fix, loop! the fix can land in any of the three layers. those fixes all look different but the cycle is the same. 3. memory is still massively unsolved, but also massively important. the hardest part is distilling learnings from traces which is incredibly domain specific. 4. when you build agents, you're probably worried about a few things: accuracy, cost, latency, and security. we've seen these themes confirmed year after year in our dev surveys 5. when we think about reducing cost, the pipeline looks like adding visibility into spend, putting controls in place, and only then pushing on agent optimizations. this is how we reduced our coding agent spend @LangChain 6. model routing, which is great for cost controls, is most effective in the harness (where the context needed for a routing decision lives) 7. open models are used for more narrow tasks right now (small parts of the agent loop OR for very specific domains w/ post-training), but as they improve they'll power more core agent loops 8. we're looking at a future with both horizontal agents and vertical agents. horizontal agents will use the vertical agents, and infra needs to change to support this load (agents can do background work at a rate that humans cannot). youtube.com/watch?v=1jDCPE… 🔗 View Quoted Tweet 💬 0 🔄 0 ❤️ 1 👀 965 ⚡
