A number is a small thing. One integer in one answer. A seating capacity, a contract limit, a delivery quantity, a tax threshold, a credit exposure. Nothing dramatic. Certainly not the sort of thing that should become an architecture problem. Then an AI agent guesses it, sounds confident, stores the guess, and uses it again later. ...
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?” ...
The agent did not forget. The system outsourced remembering. Memory sounds like a solved engineering problem until an agent has to use it for work. A customer-support agent remembers the refund policy but not why an exception was approved. A research agent retrieves the right document but loses the reasoning trail that connected three earlier notes. A workflow agent crashes halfway through a task, comes back online, and must reconstruct its own state from search results like a detective investigating a crime it personally committed. ...
A chatbot can say the right thing for ten turns and still forget what it was trying to do. That is the uncomfortable idea behind Probing the Lack of Stable Internal Beliefs in LLMs, a paper that studies whether large language models can maintain an unstated goal across a multi-turn interaction.1 The paper is not asking whether a model can avoid obvious contradictions. That is the familiar version of consistency: did the assistant say one thing on Monday and the opposite thing on Tuesday? ...
Voice demos are easy to fake. Give a model a clean recording, let it read a theatrical sentence, and the result can sound impressive enough for a launch video. That is not the hard part. The hard part is making speech generation behave like an actual product: multilingual, low-latency, emotionally credible, speaker-consistent, and not outrageously expensive to serve. ...
The spreadsheet rule that never quite reaches the model Rules are everywhere in business software. An invoice total must match its line items. A loan file must contain the right documents before underwriting. A production schedule cannot assign the same machine to two jobs at the same time. A compliance workflow may tolerate uncertainty in OCR, but not uncertainty about whether a prohibited combination of fields has appeared. ...
Discount. It is a small word, but in retail it is not decorative. It changes what people buy, how much they buy, whether they switch brands, whether they stockpile, whether distributors clear inventory, and whether a manager later pretends the promotion was “strategic” rather than simply expensive. This is where many LLM-agent demos become fragile. They can describe a discount. They can explain why a rational consumer might respond to it. They can even role-play a price-sensitive shopper with theatrical enthusiasm. But describing incentive response is not the same as simulating it. A consumer simulator that treats price as one more piece of text is not an economic simulator. It is a chatbot wearing a shopping cart. ...
Memory is usually sold as the adult upgrade for LLM agents. Give the agent a past. Give it a vector database. Give it episodes, reflections, mistakes, summaries, and a long enough context window to remember every tiny embarrassment. Surely it will become more reliable. The RPMS paper is useful because it interrupts that comforting story with a less fashionable point: memory can make an agent worse when the world has hard action rules.1 ...
Slides are supposed to be obvious. That is their entire professional excuse for existing. A good presentation does not merely contain information; it makes the intended argument recoverable by someone who was not inside the author’s head. This is why a deck can look expensive and still fail. The gradients are polished, the icons are friendly, and the narrative has quietly wandered into a swamp wearing a consultant’s blazer. ...
Mistakes are cheap until an agent repeats them. A human worker who keeps failing at the same task usually leaves traces: a blocked aisle, a missing tool, a wrong form field, an error message, a process exception. A competent manager does not simply tell the worker to “try again with more confidence.” The useful move is more boring and more valuable: identify the pattern, write the repair rule, and make sure the next attempt starts from the point of failure rather than from the beginning. ...