Monday.com gets 9x faster feedback loops by instrumenting its agents
Monday.com reports (via LangChain) 9x faster feedback loops after building evaluation and feedback automation on LangSmith — the unglamorous but decisive work of measuring and iterating on agent quality, sped up nearly an order of magnitude. The takeaway: the teams that ship reliable agents are the ones that made iteration cheap.
This is a use case about the meta-layer rather than a customer-facing bot, and that’s why it’s instructive. Monday.com’s 9x figure (LangChain-reported) is on the feedback loop — how fast they can evaluate a change and know whether the agent got better or worse. In practice that is the difference between an agent you can improve weekly and one that stalls after the demo. For a team weighing build-your-own, the lesson is to budget for evaluation tooling from day one, not as an afterthought; here that tooling was LangSmith.