Customer support bots are not supposed to have enemies. They sit politely inside enterprise websites, read policy documents, retrieve relevant snippets, and answer questions with the soft confidence of a well-trained assistant. The selling point is simple: Retrieval-Augmented Generation, or RAG, should make large language models less likely to hallucinate because the answer is grounded in external evidence. ...
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? ...
Glue is not glamorous. In most AI product discussions, the model gets the spotlight. The harness—the scripts, prompts, validators, retry rules, state files, tool adapters, and stopping criteria around the model—gets treated as plumbing. Necessary, slightly annoying, and best ignored until it leaks. That habit is becoming expensive. The paper Natural-Language Agent Harnesses argues that the surrounding execution system is no longer a secondary implementation detail. It is often the actual unit of agent performance, reliability, and portability.1 The paper’s useful claim is not that “natural language replaces code.” That would be a lovely fantasy for people who have not debugged parsers, sandboxes, or file permissions lately. The sharper claim is that part of the harness can become an editable natural-language policy object, while exact execution remains in code. ...
Consensus is comforting. That is the problem. In a meeting, consensus often means people have compared evidence, challenged assumptions, and settled on a workable answer. In a multi-agent AI system, consensus can look similar from the outside: several agents interact, exchange outputs, and converge on one shared response. The dashboard shows agreement. The workflow moves on. Everyone enjoys the small luxury of not asking what just happened. ...
A procurement workflow looks boring until an AI agent touches it. Before that moment, the process is usually wrapped in the comforting machinery of enterprise software: approval rules, validation checks, role permissions, exception paths, and enough audit trails to make everyone feel governed. Then someone inserts an agent and asks it to “handle the workflow.” The agent may know the words. It may call the right tools. It may even produce the next step that looks plausible. ...
A product listing used to have one obvious job: persuade the buyer. That buyer might be hurried, distracted, status-conscious, price-sensitive, or pretending not to care about shipping fees. Fine. Human messiness was the point. Good copywriting translated product attributes into human salience: scarcity, beauty, quality, emotion, trust. The machine’s role was secondary. Search engines ranked. Recommendation systems sorted. Humans decided. ...
A leaderboard score is useful until everyone starts treating it as a target. That is the uncomfortable business problem behind LLM Olympiad: Why Model Evaluation Needs a Sealed Exam.1 The paper is not arguing that benchmarks are useless. That would be theatrical, and not especially true. It argues something sharper: in the LLM era, a benchmark score is only as credible as the procedure that produced it. ...
Roles are convenient. They let managers believe a system is legible before it becomes messy. One agent is the compliance reviewer. Another is the customer-support representative. A third is the skeptical analyst. Add a prompt, assign a tone, define a boundary, and the organization can pretend it has converted social behavior into configuration. ...
Guidelines are not novels. That sounds obvious until we remember how most retrieval-augmented generation systems treat them. A clinical guideline becomes text. The text becomes chunks. The chunks become embeddings. The embeddings become “context.” Somewhere in that mechanical conversion, a dosing table, a referral pathway, or a threshold hidden inside a flowchart quietly loses its shape. Then everyone acts surprised when the answer is fluent but clinically thin. Very mysterious. ...
Hospital data does not politely arrive as a paragraph. It arrives as an ECG trace, an ultrasound video, a CMR sequence, a physician report, a half-remembered prior diagnosis, and a clinician trying to decide what matters before the next patient enters the room. The popular fantasy of medical AI is that a general model will simply “look at everything” and reason like a specialist. Nice fantasy. Very convenient for demo videos. Less convenient for actual cardiology. ...