LangChain details five components of its company agent harness
LangChain says internal agents for marketing, sales, and engineering converged on five shared primitives: working inside Slack, managing user or fixed identities, separating global and user memory, running code in sandboxes, and supporting scheduled or event-triggered runs. Its GTM Agent, OpenSWE, and Content Studio are built on Managed Deep Agents, which adds channels, cron jobs, sandboxes, memory, and identity infrastructure on top of the open-source Deep Agents harness. The architecture details are a first-party account, while the claimed impact on team workflows is not independently verified.
The post provides a concrete reference architecture for deploying multiple workplace agents with shared identity, memory, execution, and scheduling controls, although its capability and productivity claims remain vendor-reported.
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