When Codex launched, a team member asked what the FDE team should do once Codex could one-shot the use cases they had spent months building. At OpenAI, that question captures the purpose of the Forward Deployed Engineering (FDE) team. FDEs work alongside customers to understand their hardest problems and build solutions they can depend on, and what they learn feeds back into products and gives Research concrete problems to solve. If the team makes itself obsolete every couple of months, it is doing its job.
The team was founded in early 2025 to close what OpenAI calls the deployment gap: the distance between what models appear capable of and what people can reliably use to do their work. Sometimes the missing piece is a product. Other times, a model performs well on a benchmark but cannot handle real-world variables and constraints. Where models can already do the job, FDEs work with Product and customers to make the solution repeatable. Where they fall short, they work with customers and Research to define the gap and improve the models.
At John Deere, a 200-year-old agriculture business, the challenge was scaling personalized advice for farmers. Its See & Spray technology uses cameras and machine learning to spray weeds rather than whole fields, reducing chemical use by up to 70%. Realizing those benefits, however, depends on farmers knowing how to configure the technology and adjust their practices. John Deere deployed OpenAI's APIs to provide natural-language answers, tailored setup guidance, and alerts when equipment may not be performing at its potential.
OpenAI says these problems are often wide open, with no established ground truth and no playbook. Its FDE team operates on one core principle: start with the outcome, do what it takes to succeed, then scale. In mid-2025, around 15 FDEs worked with a large semiconductor customer on more than 10 use cases to make chip verification AI-native, an area that can account for ~70% of a project's effort.
Source: OpenAI Blog · Summarized by HeadlinesBriefing