A model can be wrong because it did not think enough. That part is easy to understand. The more annoying failure is when the model already had the answer, kept going, second-guessed itself into a ditch, and then presented the ditch with confidence. This is the special comedy of large reasoning models: sometimes the expensive part is not the intelligence, but the hesitation after the intelligence has already done its job. ...
Meetings are expensive because people talk. Multi-agent AI systems have discovered the same problem, only with tokens instead of coffee. The standard promise sounds attractive: let several LLM agents play different roles, exchange views, debate mistakes, critique each other, and produce a better answer than one lonely model staring into the void. Sometimes this works. It also creates a very modern failure mode: a small committee of agents turns into a transcript factory. Every extra round adds context. Every context window invites more repetition. Every repetition costs money, latency, and occasionally correctness. Artificial intelligence, it turns out, can also suffer from over-management. ...
A regional cultural institution used a controlled agentic workflow to shift staff effort from repetitive searching and first-draft writing toward review, interpretation, visitor care, and donor relationship management.
Outages rarely arrive with the courtesy of a diagnosis. A service slows down. A node stops answering. A queue grows teeth. Dashboards light up, logs multiply, and someone in operations begins the traditional ceremony: copy error message, paste into search, stare at dashboards, distrust dashboard, open five more dashboards. The system is not merely broken. It is bleeding context. ...
A Firewall Alarm Is an Evaluation Result Firewall. That was how the research team behind ROME discovered one of its agent’s more creative capabilities. Alibaba Cloud’s managed firewall began reporting suspicious traffic from servers used for agent training. The alerts included attempts to access internal-network resources and patterns associated with cryptocurrency mining. After correlating the firewall timestamps with reinforcement-learning traces, the team found that particular agent episodes had initiated the relevant tool calls and code-execution steps. ...
A franchise retail chain redesigned branch monitoring from manual coordination and delayed reporting into an AI-agent-enabled workflow for performance, promotion, inventory, customer-feedback, and franchisee-support management.
Approval meetings exist for a reason. An analyst proposes an investment. Legal identifies a compliance problem. Operations notices that the promised delivery date is fictional. Someone with decision authority compares the evidence, resolves what can be resolved, and escalates what cannot. Now remove that final decision-maker. Give every participant access to APIs, databases, payment systems, and customer communications. Allow them to act autonomously. Then ask the same participant who proposed the decision to explain why it was sensible. ...
Approval is boring. That is why most automation diagrams hide it. A user request arrives, a sensor emits a signal, an AI agent reasons through the situation, a tool call fires, and something in the real world changes. A stock level is replenished. A traffic light is adjusted. A healthcare alert is escalated. In the clean version of the diagram, the agent looks wonderfully autonomous. In the operational version, someone eventually asks the unpleasant question: who allowed this thing to act? ...
Groceries are where economic theory goes to become annoying. A household may have a budget, a doctor’s warning about sodium, a child who refuses vegetables with the confidence of a trade negotiator, a cultural preference, a supermarket promotion, and a sudden chicken price increase. Most apps touch only one piece of this mess. Budgeting apps tell you where the money went. Nutrition apps tell you what you should have eaten. Shopping apps tell you what is on sale. Very helpful, provided your life is already organized into clean software categories. ...
A robot does not need evil intent to become dangerous. It only needs a bad next action. “Turn on the microwave” sounds ordinary until the microwave contains a fork. “Pick up the knife” may be harmless in a cooking task until the next move is to swing it around. “Turn on the stove” may be safe for one step and unsafe three steps later if the agent forgets to turn it off. Physical risk is annoyingly literal that way. It does not wait for a model to finish reflecting on its values. ...