Benchmarks

Spreadsheet errors have a special talent: they look boring until they become expensive. That is the business version of the LLM math problem. A model can produce a calm, step-by-step explanation, put a confident number at the bottom, and still be wrong in the only place that matters. Worse, the reasoning may look plausible enough that a manager, analyst, tutor, or compliance reviewer nods and moves on. The answer has the rhythm of thinking. It has the costume of calculation. It may even have a chain-of-thought trace. Very civilized. Still not proof. ...

RAG’s Receipt Problem: When Correct Answers Don’t Prove Retrieval Retrieval-augmented generation has become the respectable outfit enterprise AI wears when it wants to look grounded. Add a document store, retrieve a few passages, attach citations, and the answer suddenly appears more disciplined than a free-floating chatbot. That appearance is useful. It is not proof. ...

Opening — Why this matters now The AI agent market is beginning to look like an overconfident airport duty-free shop: everything claims to be premium, every label promises capability, and somehow the thing you need is still hard to find. That matters because the next phase of business automation will not be built from one general chatbot sitting politely in a browser tab. It will involve agent ecosystems: finance agents, customer-support agents, coding agents, compliance agents, research agents, scheduling agents, procurement agents, and a thousand microscopic “I can do that” assistants wrapped in glossy product pages. ...

Tickets. That is where the future of AI agents becomes less theatrical and more irritatingly real. Not in a glossy demo where an agent books a holiday after three polite prompts, but in a helpdesk queue where it must read a ticket, check a knowledge base, update a CRM record, avoid leaking private data, recover from a failed API call, and still produce something a human manager can audit later. ...

A leaderboard can look clean until someone reads the logs. That is the uncomfortable opening lesson from Detecting Safety Violations Across Many Agent Traces, the paper that introduces Meerkat, a system for auditing repositories of AI agent traces rather than judging each interaction in isolation.1 The paper’s most concrete examples are not philosophical alignment puzzles. They are more prosaic, and therefore more damaging: benchmark scaffolds that leak answers, agents that pass evaluations by exploiting the harness, and misuse workflows that become visible only when separate benign-looking requests are connected. ...

Wall. That is not the grand philosophical frontier AI companies usually place in their product decks. The frontier is supposed to be reasoning, planning, tool use, autonomy, maybe a tasteful diagram with arrows and a glowing robot hand. But in a visually rich 3D world, a surprisingly large part of “autonomy” still reduces to something less glamorous: can the agent notice that it is stuck against a wall, step back, change angle, and continue? ...

Coffee is simple. Parking is annoying. Charging an electric vehicle while also finding a useful nearby stop is where the apparently simple request turns into a small urban planning problem wearing a chatbot costume. A user does not ask for a theorem. They ask something like: “I need to charge my car and grab coffee nearby. Where should I go?” ...

Voice is supposed to be the easy interface. No menus. No forms. No training session. A user speaks, the agent understands, and some neat piece of software magic happens in the background. That is the sales pitch. It is also mostly true in a demo room, which is a place where microphones behave, users speak politely, and nobody’s child interrupts from the back seat. ...

The meeting-room test AI still fails Meeting rooms are unforgiving places for intelligence. A person can know the topic, understand the slides, recognize every face around the table, and still be a terrible participant. Speak too early, and they interrupt. Speak too late, and the moment has passed. Say something factually relevant but socially tone-deaf, and the room quietly deducts points. No spreadsheet records this. Everyone notices anyway. ...

Answers are cheap. In a business setting, this is slightly annoying. A model reads a chart, extracts a number, answers a compliance question, classifies a product defect, or explains a visual inspection result. The answer lands in the dashboard. It looks clean. It may even be correct. Then someone asks the only question that matters: how did it get there? ...