How We Built LangChain’s Paid Media Agent

For LangChain’s first three years, our sales pipeline grew largely organically, driven by open source, content, YouTube, community, and meetups. In January, we wanted to kickstart our paid advertising program to start connecting with prospects we weren’t reaching organically, such as those in new regions and enterprise decision makers. We wanted to scale from a largely organic growth engine to five paid channels in just six months. This created new challenges for our small marketing team. We had to keep track of new campaigns launching across channels, different creative and targeting experiments, and a growing volume of performance data to understand and act upon. In order to scale and optimize, we had some technical hurdles to overcome. Each advertising platform has its own data schema, making it difficult to reconcile performance across channels. Campaign parameters also do not map cleanly to the outcomes we ultimately care about, such as sales inquiries, signups, or content downloads. As our product release cadence as a company accelerated and the number of campaigns grew, keeping track of what was running, what was working, and what to try next became increasingly difficult to manage manually. We set out to build an agent that could help…