A support ticket arrives with a simple request: “Can I cancel this order after the trial ends?” The AI assistant replies with a polished explanation of the company’s refund policy. The paragraph is fluent. The tone is calm. The answer is probably useful to someone. Unfortunately, it may not answer the question that was asked. ...
Translation is one of those AI use cases that sounds almost too reasonable to argue with. English medical data exist in large quantities. Many healthcare systems, researchers, and educators need non-English clinical text. Large language models are fluent, cheap, and obedient enough to produce thousands of translated reports before lunch. The spreadsheet smiles. The budget owner relaxes. The governance team is told that quality will be checked by another LLM. ...
Counseling is a bad place to hide a static AI system Customer-support bots can get away with being forgetful. They apologize, ask for the order number again, and everyone quietly lowers their expectations. Psychological counseling is less forgiving. A counselor who forgets the last session, repeats generic comfort, or treats every conversation as a fresh prompt is not merely inefficient. The whole relationship becomes unstable. Continuity is not a UX feature here; it is part of the intervention. ...
Audit logs are comforting things. They tell managers that a system took an action, they tell engineers which step fired, and they tell compliance teams that someone, somewhere, has a line of text to point at when the incident review begins. Now imagine an AI agent inside a business workflow. It has a customer request, a list of available tools, and a visible reasoning trace. The trace says it carefully considered whether to call an API, ask for missing information, or answer directly. It sounds deliberate. It sounds inspectable. It sounds like governance. ...
A support ticket does not usually arrive as a clean moral philosophy exercise. It arrives as a complaint marked urgent. Then the customer adds that a manager already approved something questionable. Then a sales team wants the answer phrased in a way that protects revenue. Then the user says there is no time to escalate. Five turns later, the AI assistant is no longer answering the original question. It is swimming inside pressure, ambiguity, and incentives. ...
Code review is a good place to hide a bad idea. One agent writes a pull request. Another agent reviews it. Two more agents look over the same thread and vote. Everyone sounds professional. The submitter explains the change as a performance improvement. The friendly reviewer raises minor cosmetic comments, because nothing says “thorough review” like asking for better docstrings while stepping delicately around the security hole. ...
Approval sounds cheap. In business systems, it is the familiar answer to almost every automation anxiety. Let the model propose, let an overseer approve, let the workflow continue. A trading agent recommends a position; a risk layer approves it. A customer-support agent drafts a refund decision; a policy checker approves it. A recommendation system optimizes engagement; a governance model approves the output. There. Safety added. Please admire the compliance architecture. ...
Tools are not free. That sentence sounds too obvious to deserve an article, which is usually a warning that the industry has built several architectures pretending it is false. A tool-using AI agent can call a search API, query a database, inspect a document, ask another model, trigger a diagnostic pipeline, or run a workflow step. In a clean demo, each call feels like another harmless unit of intelligence. The agent thinks, acts, observes, thinks again, and the audience applauds because the trace looks busy. Busy is often mistaken for capable. Enterprise software has enjoyed this little confusion for decades. ...
Prompts are now office furniture. Everyone has them. Everyone complains about them. Nobody is quite sure who owns the standard version. One team keeps a Notion page of “best prompts.” Another hides theirs in a spreadsheet. A third tells new staff to “just ask clearly,” which is not a method, but it does have the administrative elegance of doing nothing. ...
Explanation is not free. That sounds obvious until one watches an AI system in production. A model predicts. A user asks why. The platform dutifully runs SHAP, LIME, saliency maps, or some carefully branded interpretability module, then presents a ranked list of “important” features with the solemn confidence of a consultant who has just discovered a bar chart. ...