Opening — Why this matters now The AI industry has spent the last two years trying to turn large language models into workers. The result is a small circus of agents: coding agents, browser agents, research agents, support agents, spreadsheet agents, and agents that appear to exist mainly to summon other agents. Naturally, the next problem is not intelligence. It is management. ...
Agent teams are easy to draw and hard to run. On a slide, the architecture looks comforting: a planner, a researcher, a coder, a reviewer, perhaps a compliance agent standing in the corner with a clipboard. Everyone has a role. Everyone collaborates. The diagram is tidy, which is usually the first warning sign. ...
A broken file link is not usually where scientific revolutions begin. It is, however, where many automated workflows die. That is why the most revealing moment in the freephdlabor paper is not the grand claim about personalised AI research groups. It is the rather unromantic episode where the system tries to write a paper, discovers that the experiment data are missing because of a failed symlink, attempts workarounds, fails validation, reports the failure, gets routed back through resource preparation, rebuilds the workspace correctly, and only then proceeds to manuscript generation.1 ...
A bad agent stack often looks suspiciously like a bad committee. One agent proposes a plan. Another wanders into a neighbouring topic. A third confidently supplies a detail that is almost right, which is a particularly expensive genre of wrong. Then the system fuses the outputs, declares victory, and leaves the human operator to discover that “collaboration” was just error propagation wearing a nicer blazer. ...