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On October 23, 2026, Colin Jarvis, head of OpenAI's FDE team, shared the team's working approach in a blog post: the team works side by side with customers to solve the hardest problems and truly deploy AI into production environments. According to a same-day report by Wall Street See, about 15 FDEs worked with a large semiconductor customer to bring all use cases online within 3 to 6 months, saving about 48% of targeted testing time and improving median performance by 22%. After the Codex upgrade, the team converted custom applications into Codex-based plugins and skills, removing about 90% of the custom code for each project and shortening the time to extend the solution to a second team from two months to one day. There is currently no follow-up development.
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OpenAI FDE Lead Discusses How the Team Brings AI into Enterprise Production EnvironmentsDevelopments
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- Source:Wallstreetcn
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Oct 23
Wallstreetcn
OpenAI FDE Lead Discusses How the Team Brings AI into Enterprise Production Environments
Colin Jarvis, head of OpenAI's FDE team, introduced in a blog post that this team works side by side with customers to solve the hardest problems and truly deploy AI into production environments. About 15 FDEs worked with a large semiconductor customer to bring all use cases online within 3 to 6 months, saving about 48% of targeted testing time and raising median performance by 22%. After the Codex upgrade, the team converted custom applications into Codex-based plugins and skills; about 90% of the custom code per project was removed, and expanding the solution to a second team was shortened from two months to one day.
Shao Meng
How OpenAI defines FDE: consolidating customer-customized Harness into a unified Codex
OpenAI's FDE team was established in early 2025, with the goal of filling the deployment gap between model capabilities and customers' real-world constraints; the approach is to first get results into production, then consolidate customized solutions into reusable products. In the early days, every use case required extensive customized integration; after Codex became stronger, the team rebuilt the application on Codex, turning customized applications into plugins and skills based on Codex primitives, and self-reports that about 90% of the customized code for each project was removed. In a semiconductor customer case, about 15 FDEs covered 10+ use cases, self-reporting about 48% time savings in targeted test generation and about 22% median performance improvement in kernel optimization; in a telecom customer service voice case, the key evaluation rose from about 60% to 90%+ in about six months.