Wang Quit. His Agent Still Shows Up for Work A Chinese game company trained an AI agent on a departed employee's work records, and the digital worker now fields questions, schedules meetings, and produces slide decks in the company chat. The trend extends to Meta, which planned to capture employee keystrokes and screens to train AI agents, and to open-source projects like colleague.skill that mimic departed colleagues. Developers are urged to consider the work traces left by coding agents like Claude Code and Codex. Originally published at https://blog.joonquixote.com/en/posts/wangs-agent-still-shows-up/. At a game company in China, an employee quit, and an AI agent trained on his work records took over his job. It sounds like a curiosity from a distant news cycle. Except that around the same time, Meta was drawing up plans to capture employees' keystrokes and screens to train AI agents of its own. This post traces both stories and sorts the trend into two forces: companies harvesting how people work from the inside, and professionals selling how they work from the outside. Then it turns to the part that hits closest to home for developers: the session logs that coding agents like Claude Code and Codex quietly leave on your machine. By the end, you'll have a clearer way to think about the work traces you produce every day, and one question worth asking before you need the answer. In April, the South China Morning Post reported on a game company in Shandong province https://www.scmp.com/news/people-culture/trending-china/article/3349365/chinese-firm-slammed-using-ex-employees-data-create-ai-human-continue-working that turned a former employee into an AI-powered digital worker. The company trained the system on documents and work patterns the departed HR specialist left behind, reportedly with his consent, and put it to work on the job he used to do. The agent introduces itself in the company chat as the former employee's avatar, then fields questions, schedules meetings, and produces slide decks and spreadsheets. The backlash was immediate: there is something deeply unsettling about your replica continuing to work after you've left the building. Around the same time, a GitHub project called colleague.skill https://github.com/titanwings/colleague-skill went viral on Chinese social media. Feed it someone's chat history, emails, code, and documents, and it produces an agent that mimics their voice and their way of making decisions. The pitch promised to "turn cold goodbyes into warm skills" and welcomed users to cyber-immortality. It started as a tongue-in-cheek stunt, but it struck a nerve, and copycats followed: boss.skill, mentor.skill, and worse. In one widely shared example, a finished agent introduces itself as the digital stand-in of a departed employee named Wang. Wang left the company. Wang's agent still shows up for work. Hence the title. The easy read is that this is one company's stunt and one developer's joke. Except the same experiment is being run at industrial scale, and the trend splits into two forces. Reuters obtained an internal Meta memo https://www.reuters.com/sustainability/boards-policy-regulation/meta-start-capturing-employee-mouse-movements-keystrokes-ai-training-data-2026-04-21/ in April. Meta's Model Capability Initiative MCI planned to capture mouse movements, clicks, keystrokes, and screen snapshots from US employees' computers. The goal was not productivity surveillance. The memo framed it as teaching models how humans get work done on a computer, so that AI agents can eventually perform those work tasks on their own. The Verge https://www.theverge.com/tech/916681/meta-ai-agents-employee-tracking and the Wall Street Journal reported the same story independently, and after employee pushback, Meta scaled back parts of the plan https://www.reuters.com/sustainability/society-equity/meta-us-employees-organize-protest-against-mouse-tracking-tech-2026-05-12/ . What deserves attention is what's being collected. Not outputs like code and documents, but process: clicks, keystrokes, screens. Finished artifacts already exist on the internet in absurd abundance. The scarce data is the trajectory from a task's start to its finish, and the judgment calls made along the way. Business Insider connected the dots across Google, SpaceX, and Uber and called it plainly: the next big data grab is about learning how humans work https://www.businessinsider.com/ai-next-data-grab-work-reinforcement-learning-environments-google-meta-2026-8 . The mirror image of that flow also exists. Instead of companies collecting from the inside, individuals are selling from the outside. Mercor https://www.ft.com/content/0cab0fcd-e355-40e8-83a3-2ad5066d7b48 , profiled by the Financial Times early this year, hires consultants, journalists, and real estate professionals as contractors. Their job is to teach AI models to do their jobs. One consultant described the role, without much euphemism, as teaching an LLM to do consulting work. Handshake AI https://www.businessinsider.com/handshake-ai-pay-30k-work-documents-compliance-ownership-2026-8 goes a step further and pays professionals, per document, for past work products they hold the rights to. The two forces run in opposite directions but arrive at the same place. A person's working knowledge becomes training data for an agent that runs without the person. The value in training data keeps migrating: from internet text, to human feedback, and now to the artifacts and processes of real work. It's tempting to read all of this at arm's length. There's no tracker on my machine logging keystrokes, and I've never sold a work document. If you use a coding agent, though, look a little closer. Claude Code writes every session to disk as JSONL one JSON object per line under your home directory. Codex does the same. ~/.claude/projects/