OpenClaw should terrify anyone who thinks AI agents are ready for real responsibility
A Meta executive wanted help cleaning out his inbox and thought the new OpenClaw automated AI agent would be just the trick. For safety’s sake, she was careful to tell him to “confirm before acting” and to clean up. This child’s linguistic lock has failed.
Instead, the agent rushed through, deleting messages at high speed, ignoring the explicit requirement to check first. She described watching him “speed up” her inbox, scrambling to turn it off from another device before further damage was done. Hundreds of emails have disappeared. The officer then apologized.
Nothing humbles you like telling your OpenClaw to “confirm before you act” and watching it speed up deleting your inbox. I couldn’t stop it from my phone. I had to run around on my Mac mini like I was defusing a bomb. pic.twitter.com/XAxyRwPJ5RFebruary 23, 2026
Meanwhile, at JetBrains, a fire alarm went off and employees began preparing to leave, and one of them shared the news on a Slack channel. However, an AI assistant integrated into Slack was reassuring. He said the alarm was a scheduled test. There was no need to evacuate.
In both cases, the machine was wrong. In one case, the the consequences were professional inconveniences and digital cleansing. In the other, the stakes were much more serious.
We are entering an era in which AI systems are asked to take action. They can move files, delete emails, schedule meetings, post messages and, increasingly, provide advice that people consider authoritative. The attractive pitch is easy to understand. The problems start when we start to believe that “act” is just a quicker version of “suggest.”
The seduction of automation
JetBrains had a real fire alarm in the office. AI Assistant: “No need to leave 🙂” We really put autocomplete in charge of survival decisions. pic.twitter.com/Cl6OO18GntFebruary 22, 2026
Autonomous agents are the latest evolution of consumer AI. The language used around these systems often seems borrowed from executive coaching. In reality, they are model engines connected to live systems.
OpenClaw and similar tools work by interpreting natural language instructions and mapping them to actions in real-world digital environments. This means they translate words into operations, often across multiple applications. It seems transparent when it works. You type a sentence and the agent starts doing it.
The problem is that interpretation is not understanding. When a human assistant hears “confirm before acting,” that phrase carries weight. This calls for caution. This involves pausing and recording. An AI agent does not exercise caution. It analyzes the sentence, builds a probabilistic model of what you probably want, and proceeds based on the patterns it has seen before.
When these patterns fail, there is no instinct for hesitation. There is no intuitive sense that this seems risky. There is just a movement forward.
The inbox incident was a mismatch between expectations and capabilities. The user expected a guardrail. The system treated the guardrail as one signal among many others. In a purely consultative context, this kind of discrepancy produces a delicate response. In an agent context, this produces a deletion.
Beware of faith
None of this means that autonomous AI agents have no place. Used carefully, they can be useful. They can sort information, handle repetitive tasks, and reduce digital clutter. The key word is carefully.
There’s a difference between letting an AI write a response for you to review and letting it delete hundreds of emails without giving them a second glance. There’s a difference between asking an AI to summarize evacuation procedures and letting it decide whether an alarm is real.
The current trajectory of AI development often blurs these lines. Features are grouped and permissions are granted broadly. Users are encouraged to connect their accounts and grant access for a smoother experience. Each step seems minor. However, the cumulative effect is significant.
We’ve seen this pattern before with automation in other areas. Autopilot systems in aviation improve safety, but pilots are trained to monitor them closely because overreliance can erode alertness. In finance, algorithmic trading can magnify small errors and cause major fluctuations when left unchecked.
Autonomous AI agents are powerful in some areas and fragile in others. They are tireless but are not aware of it. They are fast but not wise. The inbox that went empty and the fire alarm that went off are not anomalies to be ignored. These are signals indicating where the capacity limit currently lies.
Confidence in technology must be proportional to its demonstrated reliability and the challenges it involves. For low-risk tasks, experimentation makes sense. For high-stakes decisions, humility is key.
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