LangChain详解公司智能体框架五大组件
LangChain称，面向营销、销售和工程团队的内部智能体最终采用五项共同基础能力：在Slack中运行、区分用户或固定身份、划分全局与用户记忆、在沙箱中执行代码，以及支持定时或事件触发的运行。其GTM Agent、OpenSWE和Content Studio均基于Managed Deep Agents构建，后者在开源Deep Agents框架上提供频道、定时任务、沙箱、记忆和身份等基础设施。文中架构细节来自第一方说明，而其对团队工作的影响尚未得到独立验证。
该文章给出了部署多个职场智能体时如何统一处理身份、记忆、执行和调度控制的参考架构，但能力与效率说法仍属于厂商自述。
How do you build a company harness?
We've built internal agents at LangChain across marketing, sales, and engineering, and they have completely changed how those teams work. They weren't easy to get right. We spent a lot of time solving the problems that come with a multiplayer harness, and every team landed on the same core primitives:
- It lives where people already work (for us that's Slack)
- It knows who's asking and acts OBO that person for some services and with its own identity for others
- It has memory, scoped globally and/or at the user level
- Code runs in a sandbox and can make requests to services which require auth
- Large % of its runs are scheduled rather than explicitly invoked by someone (ambient agents!!)
Thank you to @caspar_br for help on this blog
LangChain Internal Agents
To ground this discussion, a brief overview of the three main agents we have at LangChain:
#LangChain#Managed Deep Agents#Deep Agents#LangSmith Sandboxes#OpenSWE#GTM Agent#Content Studio#Slack#GitHub#Salesforce