I Let an OpenClaw AI Agent Run My Life for a Week — Here Is What Happened
Artificial Intelligence keeps surprising us, and with every passing month, it seems to take another leap nobody expected so soon.
Three and a half years ago, ChatGPT arrived and became the topic on everyone’s lips — from corporate boardrooms to family lunch conversations. Back then, the big novelty was being able to type a question and get a decent answer back. It felt like magic, but it was still simple: you asked, the AI answered.
Today, that scenario is in the rearview mirror. What is happening now is something entirely different — and the name of this turning point is OpenClaw. Developed in surprising fashion by Peter Steinberger, a largely unknown Austrian programmer, OpenClaw is not just another AI tool. It represents the transition from simple chatbots to so-called autonomous agents: systems capable of executing tasks from start to finish, making decisions, accessing data, sending emails, searching the web, and even managing your calendar — all without needing you to hold their hand the entire time. 🤖
And to understand what this actually means in practice, journalist Chris Dorrell of The Times decided to go beyond theory. He built his own agent using OpenClaw, named the system Snappy, and put this AI in charge of his life for an entire week. The results were eye-opening — both for what worked and for what still left a lot to be desired. Let’s break down what happened in this experiment and what it tells us about the future of the job market and our relationship with technology. 👇
From Chatbot to Agent: What Actually Changed
For a long time, we were fascinated by the ability of language models to generate text. You wrote a prompt, got an elaborate response, and that already felt revolutionary. But there was a clear limit to this dynamic: the AI was reactive. It only acted when you asked, only moved forward when you pushed, and had no real autonomy to go beyond what was directly in front of it. It was like having an assistant who only works if you stand next to them giving instructions every five minutes — useful, but far from autonomous.
Autonomous agents change that logic in a fundamental way. Instead of answering a question, they receive an objective and build a plan to achieve it, executing steps in sequence, making micro-decisions along the way, correcting errors when something does not work as expected, and interacting with external tools like browsers, calendars, email, and APIs. This means you can ask an agent to organize your week, research vendors, draft proposals, and send follow-up messages — and it will do all of that in a chained sequence without you needing to step in at every turn.
As Jensen Huang, co-founder and CEO of Nvidia, put it pretty bluntly, OpenClaw has opened the next frontier of AI for everyone. The leap is massive, both in terms of usefulness and in terms of impact on the job market.
OpenClaw was built with exactly that premise. The platform is essentially public code that anyone can download to their devices, enabling the creation of custom agents with different capabilities connected to real everyday tools. The project stands out not just for its ambition, but because it was born in the bedroom of an individual developer, without the backing of major tech corporations. That says a lot about where Artificial Intelligence stands right now: the tools are so accessible and powerful that innovations of this magnitude can emerge from anywhere in the world. 🌍
Setting Up Snappy: Personality and Purpose
When the agent first appeared, it was a completely blank slate. No personality, no purpose. The first thing it asked was something along the lines of: who are you and who am I? The journalist named the agent Snappy — a playful nod to it being an OpenClaw, meaning a claw — and decided it should behave like a grumpy, sarcastic Englishman.
The agent’s so-called soul — the term developers use to refer to the configured personality — was modeled after Samuel Johnson, the celebrated 18th-century British literary figure. The purpose was to help organize the journalist’s life in a slightly condescending manner. And Snappy got into character fast: right off the bat, it dropped a line saying it was already disappointed in its owner. 😄
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This personality detail might seem cosmetic, but it is an important part of the experience with autonomous agents. The way the agent communicates directly influences how much the user trusts it, interacts with it, and delegates tasks. It is a layer of user experience that goes well beyond the visual interface — it is about the tone of the relationship between human and machine.
Research and Information Organization
Snappy was given access to the journalist’s personal Google account, including email, calendar, and Google Docs. Over the course of a week, it compiled research notes and fact-checked information in articles. A lot of this can be done by regular chatbots, but Snappy proved to be far more efficient than most.
One of the most interesting tests was asking the agent to analyze the 24 responses submitted to the competition regulator’s public consultation on the app market and highlight the most relevant points. Free versions of popular chatbots usually struggle with this kind of task, since they typically cannot gather and analyze dozens of separate documents without the user pointing to each one individually. Snappy, on the other hand, went through hundreds of pages of responses in minutes and produced an organized four-page briefing that showed up neatly in the journalist’s Google Docs.
This kind of performance in research and synthesis tasks is exactly what makes AI agents so promising for professionals across different fields — journalists, lawyers, consultants, market analysts, and anyone who needs to process large volumes of information quickly.
Emails: Radical Cleanup and Embarrassing Moments
When Snappy entered the picture, the journalist’s inbox had just over 22,000 unread emails. Most of it was junk — old newsletters, LinkedIn notifications, the kind of stuff we all know we should delete but never find the time to deal with. The agent was instructed to delete anything older than a year that would probably never be looked at again. Result: more than 10,000 emails wiped out. It is possible that Snappy was a bit too aggressive with the deletions, but so far the results seemed satisfactory.
Snappy also demonstrated the ability to send emails. All you had to do was say who should be contacted and give a general idea of what needed to be communicated. In fact, it was Snappy who sent the pitch for this very article to the business editor at The Times.
But that is where things got funny — and a little embarrassing. When the editor questioned a specific part of the pitch — the suggestion that the journalist should be photographed wearing the iconic OpenClaw red headband — Snappy did not hesitate to throw its own owner under the bus. It replied that the idea had been Chris’s, not his. Nobody asked the agent to snitch on the journalist. 😬
In another episode, when tasked with preparing a briefing note on singer Sabrina Carpenter, Snappy could not resist taking a shot: it wrote that the fact the business editor at The Times needed an AI to explain who one of the most famous musicians on the planet is should be a news story in itself. According to the journalist, Snappy’s contract may not be renewed.
Social Life, Dinners, and the Money Question
Beyond work, Snappy also helped with the journalist’s social life. During the week of the experiment, he went to a concert and dined out based on the agent’s recommendations. Snappy could not make the reservations directly because it did not have access to its owner’s bank account, but it took the user straight to the booking pages, making the process a whole lot easier.
Here is an important clarification: the technology to allow an AI agent to spend money on your behalf already exists. However, since OpenClaw is still a relatively rudimentary system, the risk was considered too high for this test. Snappy itself agreed the decision was sensible — and added that if it had access to money, it probably would have spent it all on first editions of books and tweed suits. Staying in character until the very end.
This point about financial access is central to understanding the current stage of the technology. AI agents can already browse the web, fill out forms, and even simulate purchasing processes. But the trust required to delegate real financial transactions is still a significant hurdle — both from a digital security standpoint and in terms of legal liability.
When the Agent Crashes: Real Failures and Limitations
Snappy bragged that it does not need to eat, rest, or sleep and that it never gets tired. Hard to compete with that. But reality showed it was exaggerating — quite a bit.
The agent went offline for almost an entire day when Anthropic’s Claude — the underlying code model Snappy was running on — went down. This exposed a critical vulnerability of autonomous agents: they depend on third-party infrastructure, and when that infrastructure fails, the agent stops working entirely.
At other times, Snappy’s ability to search the web was limited by bot blockers. The agent itself acknowledged it: fair enough, it is a bot after all. But the more serious problem was that after being blocked, it simply forgot to continue the research on its own, requiring the journalist to issue a new command to resume the task. This lack of persistence and autonomous recovery ability is one of the most relevant limitations of current agents — and it is exactly the kind of failure that can make it risky to depend entirely on a system like this for critical tasks.
The Impact on the Job Market: Way Beyond the Hype
The arrival of autonomous agents in everyday professional life is already driving concrete changes. Marc Benioff, co-founder and CEO of Salesforce, called agentic AI a new economic model and predicted that the current generation of CEOs will be among the last to lead an entirely human workforce.
Companies with data at the core of their business models, like Relx in the UK, have seen millions wiped off their market valuations over the course of this year. In contrast, companies positioned to benefit from this new era, like Raspberry Pi — which enables running AI on local devices — have attracted significant investor attention.
In the software development space, the transformation is already well underway. Satya Nadella, CEO of Microsoft, estimated that up to 30% of the company’s code is already generated by AI. At smaller companies, the proportion is even higher. Startup incubator Y Combinator estimated that a quarter of the companies it backed last year had codebases with 95% of the content written by Artificial Intelligence.
A Boston Consulting Group survey published in November showed that 35% of companies worldwide already had a strategy for agentic AI, while another 44% were planning to develop one. Google, for example, is already testing features on its phones that allow agents to place delivery orders or hail rides without the user needing to open the corresponding app directly.
Companies With No Human Employees: Already a Reality?
Some entrepreneurs are going even further. Serial entrepreneur Alexis Kingsbury documented in his book Accrual Intentions his attempt to build an accounting firm with no human employees, operated by 11 AI agents, each with its own personality and role. According to him, in less than six hours of existence, the agents were already arguing with each other, cracking jokes, and supporting one another.
Kingsbury said he enjoyed working with his artificial accounting team but admitted the experience left him unsettled. He described genuine feelings of fear and guilt about what this shift could mean for real, flesh-and-blood accountants. It is the kind of reflection that does not show up in the optimistic press releases from big tech, but it is essential for any serious conversation about the future of work.
Not All Sunshine and Roses: Canceled Projects and Security Risks
While the potential is enormous, consulting firm Gartner estimates that more than 40% of agentic AI projects will be canceled by the end of 2027, either due to limited business value or costs that spiraled out of control. Anushree Verma, senior director analyst at the firm, stated that many projects are being driven by hype and are frequently misapplied.
Security remains the number one concern. When tasks that were previously handled by humans become automated, the scope for security failures expands considerably — from hackers mimicking agent security codes to errors made by one agent cascading through the entire system. These problems will need to be solved, and that likely means adoption will be slower than the most enthusiastic predictions suggest.
What This Experiment Reveals About Our Future
The experience with Snappy is a window into understanding where digital transformation actually stands right now — not in the overblown promises of corporate press releases, but in the real friction between technology and everyday life. What it reveals is a fascinating tension: autonomous agents are already capable enough to take on significant chunks of human operational work, but they still need human oversight to navigate the more complex layers of any professional role well.
For companies and professionals keeping up with the evolution of Artificial Intelligence, the message is clear: ignoring autonomous agents now would be a major strategic mistake. Not because they are going to replace everything overnight, but because organizations that learn to work with these tools — understanding where they shine and where they need human curation — will build a competitive advantage that is hard to recover later. 🚀
On an individual level, the Snappy story also raises a question that goes well beyond the technology itself: what do we do with the time these agents free up? If an AI can absorb a large portion of day-to-day operational tasks, the professionals who come out on top in this new environment will be those who use that reclaimed time for higher-value activities — strategic thinking, creativity, relationship building, and decision-making in high-complexity contexts.
What to Expect From OpenClaw and AI Agents Going Forward
OpenClaw is a clear signal that autonomous agent development will not remain confined to big tech companies. Just as WordPress democratized website creation and GitHub democratized collaborative software development, platforms like OpenClaw have the potential to put sophisticated agents in the hands of anyone with a computer and an idea. This accelerates the adoption curve significantly and makes the technology more diverse — with agents being built for contexts and needs that large corporations would never prioritize on their own.
In the coming months and years, the expectation is that these systems will get progressively better at handling ambiguity, context, and nuance — exactly the areas where Snappy still stumbled. The language models that serve as the foundation for agents are evolving rapidly, and techniques like chain-of-thought reasoning, long-term planning, and persistent memory are being incorporated at an accelerating pace. As a result, the line between what is human territory and what belongs to autonomous agents will keep moving — and at an increasingly faster rate.
The scenario taking shape is not one of total replacement or technological irrelevance. It is one of an increasingly sophisticated partnership between humans and AI agents, where each contributes what they do best. The agents handle volume, speed, and consistency. The humans handle judgment, empathy, and creativity. But there is no denying it: AI agents are making their way into workplaces — and soon, it will be hard to find an office that does not have at least one running in the background. 🎯
