AI Agents

Review is cheap until it has to be correct. That is the uncomfortable lesson behind many agentic AI demos. A system writes an answer. A second model checks it. A third model fixes it. The workflow looks reassuringly managerial, like a tiny consulting firm trapped inside a GPU cluster. But the appearance of oversight is not the same thing as oversight. A weak reviewer can punish a good answer. A weak fixer can damage a nearly correct answer. And if the whole chain receives one final reward, reinforcement learning may end up congratulating the wrong participant. Very corporate, really. ...

Every enterprise AI team eventually meets the same annoying bill: the agent that thinks too much. It calls tools when a direct answer would do. It loops through evaluator prompts for tasks that need one clean instruction. It drags a code interpreter into a problem that is mostly reading comprehension. Then, after all that expensive theatre, it may still be wrong. Very impressive. Very modern. Very invoicable. ...

When Agents Compare Notes: How Shared Memory Quietly Rewires Software Development Software teams already know the problem. One developer discovers the weird edge case. Another developer repeats the same mistake three weeks later. A third person writes a Slack explanation that disappears into the corporate sedimentary layer, next to the launch checklist from 2019 and that one blessed Docker command nobody can find anymore. ...

A game engine is usually where rules go to become software. Someone writes the rules, someone else encodes the rules, and an AI agent then spends its expensive little life asking the engine what moves are legal, what happens next, and whether it has already lost. Very glamorous. Very repetitive. General Game Playing tries to remove the hand-built engine from that loop. Instead of building a custom simulator for chess, backgammon, Amazons, Reversi, or some procedural oddity invented on a tired Wednesday afternoon, a game is described in a formal language and a generic system turns that description into something agents can use. ...

TL;DR for operators FaithAct is useful because it changes the unit of control. Instead of asking whether a multimodal model’s final answer is correct, it asks whether each intermediate claim is supported by the image before that claim is allowed to steer the next step.1 That is a more operational target. Accuracy tells you whether the system arrived somewhere acceptable; perceptual faithfulness tells you whether it drove through the road or hallucinated a bridge. ...

Personas have always been the slightly embarrassing cardboard cut-outs of product strategy. A marketing team invents “Sarah, 34, urban professional, values convenience.” A UX team adds “busy mother of two.” Someone in sales insists she is “budget-conscious but aspirational,” because apparently every fictional human being is. Then everyone nods solemnly and uses Sarah to justify a pricing page, an onboarding flow, or an ad campaign. ...

A research workflow usually starts clean. The first search is sensible. The first source is relevant. The first reasoning step looks promising. Then the agent opens five webpages, follows a few tangents, remembers an early mistake too faithfully, and keeps dragging the whole mess forward like a consultant who refuses to delete old slides. By the time the problem actually becomes difficult, the model is no longer short of information. It is drowning in it. ...

Phones are where automation goes to embarrass itself. A desktop workflow can often be forced into a neat sequence: open tab, click menu, submit form, pretend the enterprise software was designed by someone who likes people. Mobile apps are less polite. They hide features behind drawers, gestures, modals, permissions, scrolling lists, bottom sheets, dark-pattern-ish confirmations, and the occasional button that looks decorative until it suddenly matters. A human user handles this with a mixture of visual attention, memory, muscle habit, and mild resentment. A mobile control agent has to do it with pixels, UI trees, and a policy that decides where the next finger should land. ...

Engineering teams know this ritual too well. A promising simulation model works on one equation, collapses on the next geometry, behaves politely in the loss curve, then quietly vandalises the boundary conditions. Someone adjusts the architecture. Someone changes the sampling schedule. Someone adds a physics-informed loss term. Someone discovers, three days later, that the clever idea was mostly a tensor-shape bug wearing a lab coat. ...

Delay is not a footnote in automation. It is the product. A customer support agent that takes thirty seconds to decide whether to escalate has already shaped the customer’s mood. A warehouse robot that produces the correct plan after the pallet has moved has produced something closer to poetry than control. A trading assistant that generates a gorgeous hedge after the market has repriced is not sophisticated. It is late, which is the expensive version of wrong. ...