改进AI智能体本质是数据挖掘问题
LangChain的Viv分享了一套改进智能体的实战思路，核心是用生产数据挖Evals，还给了Harness-PostTrain的开源模型配方。
LangChain的Viv在AI Engineer World Fair上分享持续改进智能体的方法论。他认为智能体会遵循Evals中编码的行为，因此高质量Evals来自大规模挖掘生产数据。
他还提出了用开源模型搭配Harness与PostTrain的“三明治”配方来定制智能体系统。演讲强调不存在通用模型或通用Harness，针对具体任务优化模型与框架组合效果更好。
Improving agents is a data mining problem ⬇️ Viv @Vtrivedy10 the @aiDotEngineer World Fair always one of the best events every year to talk to builders at the frontier of Research, Agents, Evals, Systems, etc A few weeks ago I gave a talk on - Continually Improving Agents - building Agents to understand data from other Agents - & a walkthrough of some of our latest work on data agents & post-training experiments some fun takes: - Every Continual Learning company will be an Observability & Eval company (and vice versa) - Environments & Evals are the currency of agent improvement. Agents are literally following the behaviors encoded in Evals. The best way to make good evals is mining Production data at scale - A good recipe to own your intelligence is using a Harness Eng - PostTrain - Harness sandwich with open models - Model-Harness-Task fit! There is no universal model or universal harness. You can always build a better agent system by optimizing the model and harness for a given task if your team is looking to understand your data at scale, build environment/evals, or just improve your agents - reach out, hmu would love to work with you! 🚀 youtu.be/CvRngaQZQ3Y?is… 0 🔗 View Quoted Tweet📊 2 ⚡
This report is categorized under AITOP's "How-to" desk. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include LangChain, agents, data mining, Evals.
Source attribution: curated from LangChain (@LangChainAI).
