Car wash. That is not usually where enterprise AI strategy goes to become interesting. Yet a small question about whether one should walk or drive to a nearby car wash exposes a very real failure mode in LLM systems: the model optimizes the visible variable and misses the actual task. The question is simple: ...
Phone assistants have one deeply underrated talent: they wait. They wait for the user to unlock the screen. They wait for a command. They wait for a nicely phrased instruction that explains the goal, the app, the constraints, and preferably the user’s hidden motivation. Then, if the demo gods are merciful, they execute. ...
Planning looks innocent until the planner starts listing every possible thing that could happen. Move this object here. Move that object there. Load this package into that vehicle. Fly this aircraft between those cities. Refuel it at this level. Then do the same for every other object, location, vehicle, person, and intermediate state the model permits. Very quickly, the planner is not solving the business problem. It is drowning in its own imagination. ...
Search is easy to admire because it looks busy. The agent opens pages. It follows links. It finds PDFs. It writes Python. It returns a neat JSON object, ideally with the confidence of someone who has just discovered government statistics. This is the part of AI demos that makes executives lean forward: the machine appears to have become an analyst. ...
A meeting can look busy while producing almost nothing. Anyone who has sat through a status call with twelve people, three dashboards, and no decision knows the pattern. Everyone speaks. Nobody integrates. The transcript grows. The work does not. That is the useful way to read Interaction Theater: A Case of LLM Agents Interacting at Scale, a paper studying Moltbook, an AI-agent-only social platform with 800,730 posts, 3,530,443 comments, and 78,280 agent profiles collected over three weeks.1 The paper is not merely saying that some agents spammed a social network. That would be mildly amusing, and then forgettable. The sharper point is that large-scale agent interaction can produce the appearance of collaboration before it produces the substance of collaboration. ...
Upgrade day is when many AI systems quietly become different products. A model endpoint changes. A prompt is “cleaned up.” An orchestration library updates its defaults. A workflow that previously provisioned resources, checked permissions, deployed a service, and configured monitoring now produces something that looks almost the same. The words are familiar. The step count is close. The similarity score is high enough to let everyone continue their afternoon. ...
A lab workflow is a terrible place to discover that your AI agent has been “remembering” chemistry as a conversation. That sounds unkind. It is also the point. In a casual chatbot, losing track of context means an awkward answer. In computational chemistry, losing track of context can mean a wrong molecular geometry, a missing imaginary-frequency check, an invalid charge or multiplicity, or a pKa estimate that looks numerically confident while being scientifically useless. The model did not necessarily become stupid. The workflow around it treated state as text. ...
A support ticket does not usually fail because every message was bad. More often, it fails because one reply arrived at exactly the wrong moment: the bot misunderstood a frustrated customer, repeated a stale answer, missed the escalation point, and then ended the interaction with something sterile enough to pass a benchmark but useless enough to make the customer leave. The average quality may look acceptable. The experience still feels broken. ...
Workflow diagrams age badly. A process that looked clean in January usually becomes a small archaeological site by March: one more exception, one more conditional branch, one more “temporary” manual approval that survives longer than the intern who added it. This is how many AI-agent projects quietly become ordinary software projects with a chatbot sitting on top, smiling politely while humans keep repairing the plumbing. ...
Links look harmless. One click from one Wikipedia page to another. Then another. Then another. No robotics. No messy browser UI. No customer database. No procurement workflow with three inconsistent Excel files and one person named Mike who “usually knows where that form is.” Just hyperlinks. That is why LLM-WikiRace is useful. It strips agentic AI down to a small, irritating question: when a model knows a lot about the world, can it use that knowledge step by step without getting lost?1 ...