OpenAI's dot assistant burned 300K+ tokens in a day without a single prompt

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.

Compared to other personal agents, however, OpenAI’s dot “looked a little bit different,” Hanah-Marie Darley, Geordie chief AI officer, told The Register.

“One of the things that we discovered really quickly was that they were really, really active without much user interaction.” 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.

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.” '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.

“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.