AI Agents

Video is a terrible place to hide evidence. Not because the evidence is invisible. Because it is usually obvious only after someone has already found the right minute, the right scene, and the right visual detail. A person reviewing a long customer-support screen recording, a training video, a compliance recording, or a surveillance clip rarely watches everything with equal attention. They skim, localize, zoom in, check the detail, and then answer. Primitive, yes. Effective, also yes. ...

A simulation job does not care how elegant the prompt was. It cares whether the input files are valid, whether the parameters are compatible, whether the previous step produced the right intermediate state, whether the solver converged, and whether the final number actually means what the workflow says it means. This is where the romance of “AI scientists” usually meets the concrete wall of scientific computing. The model can sound like a postdoc. The machine still wants the correct INCAR tag. ...

Waiting is a decision. That sounds obvious to anyone who has watched a warehouse robot pause at an intersection, a trading system delay execution, or an autonomous vehicle slow down before a pedestrian crossing. In the real world, “do the task” is rarely the whole instruction. The operational instruction is closer to: do the task, in this order, not before this condition, not after that deadline, and preferably without wasting time while pretending that nothing is happening. ...

A taxi is a useful little trap. It looks harmless: pick up passengers, drive them to destinations, do not run out of fuel. A small grid-world taxi environment is not exactly the sort of thing that makes executives whisper “agentic transformation” over terrible conference coffee. But that is precisely why it works. Strip away the enterprise theatre, and sequential decision-making becomes easier to see. An agent observes a state, chooses an action, receives the next state, and repeats. If two agents always make the same moves and achieve the same objective, most organizations would treat them as equivalent. Same behavior, same operational meaning. Audit passed. Ship it. ...

Let There Be Light (and Agents): Automating Quantum Experiments A lab notebook is not just a diary. It is an institutional memory system with bad handwriting, missing parameter values, and occasional coffee damage. That is not a joke, unfortunately. In experimental science, much of the valuable knowledge sits between formal theory and physical execution: which crystal goes with which pump, how the beams should be routed, which detector timing window is plausible, which old setup can be reused, and which beautiful simulation is quietly lying through its teeth. ...

Repetition is where most automation systems quietly embarrass themselves. Ask an AI agent to book a hotel once, and it may inspect the screen, reason through options, click through menus, and eventually finish the task. Ask it to do something similar tomorrow, and many systems perform the same little theatre again: perceive, reason, click, wait, reason, click, apologize, recover. Very intelligent. Very expensive. Slightly absurd. ...

The city does not answer literal questions A person says, “I’m thirsty.” A human does not usually reply, “Please specify whether you require a vending machine, café, convenience store, supermarket, juice shop, water fountain, or bubble tea store.” That would be technically attentive and socially catastrophic. A human looks around, remembers what cities usually contain, infers which places can satisfy the need, and starts walking toward a plausible target. ...

A green block, a yellow block, and a very small number Green on yellow. That is the task. A tabletop robot sees a green block, a yellow block, and a few other objects. It has low-level manipulation skills. It receives a high-level instruction: put the green block on top of the yellow block. This sounds like exactly the kind of small benchmark task that modern AI agents should now handle with theatrical confidence. ...

Benchmarks are clean. Research is not. A benchmark asks a model to answer a question, then politely stops. A research workflow asks the model to form a hypothesis, test it, read the result, notice what went wrong, adjust the plan, and try again without wandering into scientific nonsense. One is a quiz. The other is a beaker with a budget, a deadline, and a surprisingly expensive simulation queue. ...

Simulation software has a talent for hiding intelligence inside inconvenience. A mature physics code may contain decades of numerical insight, community testing, and domain expertise. Then it asks the user to prove loyalty by editing parameter files, remembering command sequences, managing mesh directories, choosing execution binaries, checking output folders, and pretending that none of this is a productivity tax. This is not because scientists enjoy suffering. Mostly. It is because high-performance scientific software often grows around capability first and usability later. ...