Oumi的边疆AI工程师现在建造了AI agents，并重现其背后的模型
企业人工智能平台公司Oumi PBC今天扩展了其Frontier AI工程师agent，从而可以为客户建造AI agents，并重新训练那些agents运行的模型。
发布会追随一个问题，一旦agents离开演示阶段，这个问题往往会浮出水面。 一个失败的agent可能有一个糟糕的即时，破损的工具或者一个薄弱的基本模型。
Oumi 称企业传统上试图用快速和驾驭工程加以修复，因为再培训模型的重量需要通常在前沿AI实验室找到的技能。 这种方法往往在agent可靠到能够发挥作用之前就停滞不前。
Oumi的Frontier AI工程师已经为其平台上的定制模型设定目标，设计评价并开展培训实验。 随着今天的更新，一个团队可以用简单的语言来描述它想要agent完成什么，FAiE帮助定义agent在进入评价和培训前需要的工具和环境。 agent投入生产后，无论出什么差错，都会变成下一轮的评价集和培训数据。
联合创始人兼首席执行官马纳斯·库库米迪斯·称更好的提示和更大的通用型号将只能占用agents至今。 称指出失败的任务和用户偏好是重要的信号。
已经经营agents的公司可以保留在那里。 顾客的管弦乐、工具与记忆都保留下来，模型最终每天在同一环境agent工作。
Oumi也在为它已经支持的被监督的微调添加直接偏好优化，因此可以对一个模型进行关于行为用户偏好的例子的培训。 强化学习通过组合相对政策优化而实现，该组合为任务成果定级模式。 几种方法可以在单一的多阶段训练中连结在一起，FAiE帮助选择策略，设置奖励和诊断沿途的问题。
训练工作为Oumi关于模式所有权的更广泛论点提供了依据。 大部分企业agents运行于前沿AI供应商的通用模型上，自三月发布为定制型号的商业平台以来
集团相对政策优化是发布在私人预览版，西雅图公司正在接受访问请求。 Oumi是一个风险资本支持的创业者，融资是2025年1月由Venrock Management and Obvious Management Services牵头的1000万美元的种子回合。 创始团队包括Google有限责任公司Gemini集团的前工程领导人。
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Oumi’s Frontier AI Engineer now builds AI agents and retrains the models behind them
Enterprise artificial intelligence platform company Oumi PBC today extended its Frontier AI Engineer agent so it can build AI agents for customers and retrain the models those agents run on.
The release goes after a problem that tends to surface once agents leave the demo stage. A failing agent might have a bad prompt, a broken tool or a weak underlying model.
Oumi says enterprises have traditionally tried to fix it with prompt and harness engineering because retraining a model’s weights has required skills normally found at frontier AI labs. That approach often stalls before the agent is reliable enough to put to work.
Oumi’s Frontier AI Engineer already sets objectives, designs evaluations and runs training experiments for custom models on its platform. With today’s update, a team can describe in plain language what it wants an agent to accomplish, and FAiE helps define the tools and environments that agent needs before moving on to evaluation and training. Whatever goes wrong once the agent is in production is turned into evaluation sets and training data for the next round.
Co-founder and Chief Executive Manos Koukoumidis said better prompts and bigger generic models will only take agents so far. The ones that succeed will improve “by training the weights of the models powering them, learning from their own work,” he said, pointing to failed tasks and user preferences as the signals that count.
Companies that already run agents elsewhere can keep them there. Under a feature Oumi calls Bring Your Own Harness, the customer’s orchestration, tools and memory stay put and connect to Oumi for evaluation and model training. Models end up tuned in the same environment the agent works in every day.
Oumi is also adding direct preference optimization to the supervised fine-tuning it already supported, so a model can be trained on examples of the behavior users preferred. Reinforcement learning arrives through group relative policy optimization, which grades a model on task outcomes. Several methods can be chained together in a single multistage training run, and FAiE helps choose the strategy, set up rewards and diagnose problems along the way.
The training work feeds Oumi’s broader argument about model ownership. Most enterprise agents run on general-purpose models from frontier AI providers that aren’t optimized for a particular customer’s work. The company’s pitch since launching a commercial platform for custom models in March has been that a specialized model the customer owns outright can do better work at lower cost.
Group relative policy optimization is launching in private preview, and the Seattle company is taking requests for access. Oumi is a venture capital-backed startup that raised a $10 million seed round led by Venrock Management and Obvious Management Services in January 2025. The founding team includes former engineering leaders from Google LLC’s Gemini group.
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