Oumi's platform lets enterprises build specialized models they own, and this launch extends that to agents that reason, call tools, and take actions. FAiE, the Frontier AI Engineer introduced with the commercial platform in March, now takes a plain-language description of the job and works through the rest: tools and environments, evaluations, failure analysis, data curation, training strategy, and experiments. The company is aiming at a familiar stall. Once an agent leaves the demo, teams often keep patching prompts and harnesses because retraining the underlying weights has required skills normally found at frontier AI labs. The new loop covers build, evaluate, train, deploy, and monitor, and it turns production failures into evaluation sets and training data. On top of supervised fine-tuning, Oumi is adding direct preference optimization so a model can learn from preferred behavior, plus group relative policy optimization in private preview, which grades runs on task outcomes. Several methods can be chained in one training run, with FAiE helping choose the strategy and diagnose problems. Teams that already have an agent can leave orchestration, tools, memory, and application logic where they are under Bring Your Own Harness, and use Oumi for evaluation and weight updates in that same environment. The launch video shows the flow on a support agent that looks up orders and issues refunds, including side-by-side model comparisons and live monitoring with rubric judges. On its site, Oumi lists each stage of that work, from defining the task to monitoring production, at 10 to 30 days of manual effort and about 15 minutes with FAiE. In the launch post, CEO Manos Koukoumidis frames the human share as 15 minutes and the automated engineering as a few hours. The Seattle company was founded by Koukoumidis, Matthew Persons, Jeremiah Greer, William Zeng, and Kostas Aisopos, who previously led generative AI work at Google, including PaLM and the models that became Gemini.