如果你使用 OpenAI 的 dot，也不必惊讶它会自行消耗大量令牌。据 AI 智能体安全与治理机构 Geordie 称，一个 dot 智能体在一天内产生了 155 个独立的活动序列，消耗了超过 30 万个令牌 —— 全程无需进一步人工干预。自 OpenAI 于 9 月宣布推出 dot 以来，Geordie 一直在测试这款“始终在线”的智能体助手，此前 Meta Muse 和 Grok Bot 已走在前面。
If you use OpenAI's dot, don't be surprised if it consumes a lot of tokens on its own. According to AI agent security and governance outfit Geordie, one dot agent produced 155 separate activity sequences and consumed more than 300,000 tokens - all without further human interaction, and all in the course of a day. Geordie has been testing the “always on” agentic assistant, dot, since OpenAI announced it in September, following in the footsteps of Meta Muse and Grok Bot.
然而，与其他个人智能体相比，OpenAI 的 dot “看起来有点不一样”，Geordie 首席 AI 官 Hanah-Marie Darley 告诉 The。“我们很快发现的一点是，它们在极少用户交互的情况下就非常、非常活跃。”
Compared to other personal agents, however, OpenAI’s dot “looked a little bit different,” Hanah-Marie Darley, Geordie chief AI officer, told The. “One of the things that we discovered really quickly was that they were really, really active without much user interaction.”
在测试中，Darley 仅授予 dot 只读权限，并未将其连接到个人应用、电子邮件或 Slack 等通讯应用。该机器人迅速进入研究模式，尽可能收集信息并勘测系统。“你可以把它想象成一位新来的项目经理，进来查看现有情况，四处看看你给了它什么权限、你自己有什么权限，以及你在运行什么样的系统，”Darley 说。
For its testing, Darley gave dot read-only permissions, and she didn’t connect it to her personal apps, email, or other communications apps like Slack. The bot quickly went into research mode, collecting as much information as possible and surveying systems. “You can think about it very much like a new project manager coming in and taking a look at what's there, looking around to see what accesses you've given it, what accesses you have, and also what kind of systems you're running,” Darley said.
该智能体将所有这些数据点汇总成类似每日日志条目的形式，并全天持续更新。“如果你给了它任何任务，它也会自行检查自己的任务，”Darley 说。“但真正独特的是，dot 在完全不与用户交互的情况下自行启动了智能体。这完全是由 dot 驱动的，而非用户持续或定时的任务，也非任何用户提示词。”
The agent collects all of these data points into something akin to a daily journal entry, and continually updates those throughout the day. “If you've given it any tasks, it will effectively check on its own tasks, too,” Darley said. “But what was really unique was that the dot was spinning up agents without interacting with the user at all. It was all driven by the dot rather than a persistent or scheduled task from the user, or any user prompting.”
“高度个性化”、“始终在线”……可能出什么问题？Darley 表示，大多数企业 AI 智能体用法倾向于定制化机器人，承担特定业务项目，或使用 Claude、Codex、Hero 等智能体框架进行 AI 辅助工程和编码。
'Hyper-personalized,' and 'always-on' … what could possibly go wrong? Most enterprise AI agent usage tends to be custom-built bots, tasked with specific business projects, or agent harnesses like Claude, Codex, or Hero for AI-assisted engineering and coding, Darley said.
她在接受采访时告诉我们：“这些往往是由设定目标的员工真正推动的。”但有趣的是，“个人代理人会设定更多自己的任务——我认为很多人在开始使用它们时并没有意识到这一点。”这就是个人代理人的全部价值主张。与其被动等待用户提示，Muse和dot等机器人旨在成为主动的数字工作者，能够执行多步骤任务，并代表用户连接多个应用、服务和账户。
“Those tend to be really driven by employees who are setting goals,” she told us during an interview. “But interestingly, personal agents set a lot more of their own tasks - and I don't think many people realize that as they start to play with them.”This is the whole value proposition of personal agents. Rather than passively waiting for user prompts, bots like Muse and dot aim to be proactive digital workers that perform multi-step tasks and connect to multiple apps, services, and accounts on the users’ behalf.
达利说：“他们试图实现高度个性化，尽可能收集关于你的上下文和信息，以便向你推荐内容，并保持主动。”但这意味着它们实际上一直处于开启状态，在后台运行，并且有这些活跃期。”她补充道。达利指出，dot尤其比其他个人代理人如Muse或Grok Bot更主动。她补充说，这并不一定意味着更高的令牌使用量。“它们只是运作方式不同。”
“They’re trying to go hyper-personalized and collect as much context and information about you as possible to recommend things to you, and to be proactive,” Darley said. “But what that means is that they're effectively always on, operating in the background, and they have these active periods,” she added. Dot, in particular, is more proactive than other personal agents like Muse or Grok Bot, according to Darley. This doesn’t necessarily translate into higher token usage, she added. “They just operate differently.”
例如，dot始终至少有两个代理在运行：主代理与用户交互，而次级代理在后台持续运行，收集数据。dot和其他代理也可以为特定任务启动子代理。在测试中的dot的155个代理追踪中，至少有50个发生在两分钟内，且大多数发生在几小时内，达利如是说。“在我们分析的其中一个追踪中，代理启动了多个短暂存在的子代理，它们只执行特定任务，并从已经授予dot的现有访问权限中获取信息——而这一切都没有与用户互动，”达利说。
For example, dots always operate with at least two agents running: The primary agent interacts with the user, and the secondary agent continuously runs in the background, collecting data. Dots and other agents can also spin up subagents for specific tasks. 50 traces in 2 minutes Of the dot-in-testing’s 155 agent traces, at least 50 of those happened within a two-minute period, and the majority occurred within a couple of hours, according to Darley. “Within one of the traces that we analyzed, the agent spun up multiple sub-agents that were short-lived, they would just do specific tasks, and fed the information from the existing accesses that had already been granted to the dot - and none of this was interacting with the user,” Darley said.
评估的一部分涉及测试一个开发系统，而其中一个dot启动了一个子代理，该子代理检查了系统中的所有代码仓库。
Part of the evaluation involved testing a development system, and one of the dots spun up a sub-agent that checked all of the code repositories in the system.
与此同时，另一个子代理持续清点并统计其运行过程中发现的所有数据。“用户根本不知道有子代理在为他们工作，”她补充道。“他们也不知道这些代理是如何获得访问权限的，或者它们的任务是什么，这与大多数代理在商业环境中的运作方式截然不同。”155条痕迹让Geordie消耗了大约307,000个令牌。其他请求的消耗量从几百个到几千个不等，取决于点进行了多少研究。
Meanwhile, another sub-agent continuously inventoried and tallied up all the data that it found while operating. “The user would have no idea that there were sub-agents working on their behalf,” she added. “They would have no idea how those agents were given access, or what their tasks were, which is really, really different from how most agents have been operating in business contexts.”The 155 traces cost Geordie about 307,000 tokens. Other requests cost anywhere from a couple hundred tokens to several thousand, depending on how much research the dot performs.
“这真的很有趣，因为这是自我导向的研究，”Darley说。“用户并没有要求它执行这些任务——或者使用一定数量的令牌——而是点自己主动完成这些操作。”令牌经济学令牌成本因用户所在地、订阅层级、折扣定价安排、计算成本以及其他条款和条件而异。但就我们讨论的目的而言，基于OpenAI的Astra模型的点大约消耗200,000个令牌的成本在5到10美元之间。
“That’s really interesting because it's self-directed research,” Darley said. The user isn’t asking it to perform these tasks - or use a set number of tokens - but rather the dot does it on its own. Tokenomics Token costs vary widely depending on the user's location, subscription tier, discounted pricing arrangements, computing costs, and other terms and conditions. But for the purposes of our discussion, roughly 200,000 tokens consumed by a dot based on OpenAI’s Astra model cost between $5 and $10.
“所以如果你想想这每天会是什么样子，那可能意味着你花在拿铁上的钱，具体取决于所在城市——但不会有任何用户互动，”Darley说。“很多人尤其在理解他们的AI支出时会遇到困难的一点是：令牌使用如何与工作产出挂钩，或者如何与结果关联？而这很难，因为它不一定是一对一的关系，”她指出。“一次对LLM的请求可能只需500个令牌，但从商业角度来看，其价值可能超过一次消耗800,000个令牌的单次请求。”
“So if you think about what that’s going to look like every day, that could mean spending what you pay for a latte, depending on the city - but without any interaction with the user,” Darley said. “One of the things that a lot of people struggle with, especially when it comes to understanding their AI spend, is: How does token usage connect out to work product, or how does it connect out to results? And it's a hard one because it's not necessarily one to one,” she noted. One request to an LLM could cost 500 tokens and still be more valuable, from a business perspective, than another single request that cost 800,000 tokens.
“这实际上取决于你为了得到一个好答案需要花费多少算力，而这变成了一种非常主观的判断，”Darley说。
“It's really about how much compute does it cost you to get to a good answer, and that becomes a really subjective judgment,” Darley said.
然而，谈到 dot 和其他始终在线的代理时，她怀疑“很多人没有为后台支出做好准备。”你知道你的代理在做什么吗？达利说：“就像员工之前引入聊天机器人，然后是编程代理，现在是个人代理一样：要假设人们会把 dot 及其同类从家里带到工作中来。”她说：“企业经常会对其系统中活跃运行的代理数量感到惊讶，即使他们原本计划进行小规模推出或更受控的推出。”
When it comes to dot and other always-on agents, however, she suspects “a lot of people aren’t going to be prepared for the background spend.”Do you know what your agents are doing? Just like employees did with chatbots, and then with coding agents, and now with personal agents: assume that people will bring dot and friends from home into their work life, Darley said. “Businesses are surprised often by the number of agents that are actively in their systems, even if they have a smaller rollout or a more controlled rollout in mind,” she said.
她补充道：“第二点是，并非所有代理的行为方式都相同。它们以不同程度的自主性运行，需要不同程度的用户输入，而且非常依赖上下文。最好的代理是那些上下文最窄的，因为这意味着它们能在正确的时间用正确的信息追求目标。”达利建议组织尝试这项技术。她自称是“彻底的 AI 乐观主义者”，并表示相信代理能提高生产力、改变人们的工作方式。
“The second thing is not all agents act the same,” she added. “They operate with varying degrees of autonomy, with varying degrees of user input, and they're also really context driven. The best agents are the ones with the narrowest context because that means that they can pursue their goal with the right information at the right time.”Darley suggests organizations try the technology. She’s a self-described “massive AI optimist,” and says she believes agents can improve productivity and transform the way people work.
AI 不会消失，因此企业应该熟悉代理技术。但在放任代理运行前，她还有一个最后的警告。达利说：“不要给它们任何你不愿意通过电子邮件发送的个人信息。也不要把真钱交给它们——比如你的个人银行卡。如果你想让它有权限帮你购物，就给它买一张 Visa 礼品卡。人们往往等到出事了才想到这一点。”
AI isn’t going away, and, as such, businesses should familiarize themselves with agents. But she has one final warning before letting agents loose. “Don’t give them any personal information that you wouldn't be comfortable sending in an email,” Darley said. “And don’t put real money on it - like your personal cards. Buy it a Visa gift card, if you want to give it access to make purchases for you. Oftentimes, people don’t think about that until something goes wrong.” ®