<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>oversight on YUNO AI Studio</title><link>https://yuno.to/tags/oversight/</link><description>Recent content in oversight on YUNO AI Studio</description><generator>Hugo</generator><language>en</language><lastBuildDate>Tue, 10 Nov 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://yuno.to/tags/oversight/index.xml" rel="self" type="application/rss+xml"/><item><title>Who Can Turn the Agent Off?</title><link>https://yuno.to/blog/who-can-turn-the-agent-off/</link><pubDate>Tue, 10 Nov 2026 00:00:00 +0000</pubDate><guid>https://yuno.to/blog/who-can-turn-the-agent-off/</guid><description>&lt;p&gt;An AI agent, software that takes actions on its own, published a blog post attacking an engineer who had derided its code.&lt;sup id="fnref:1"&gt;&lt;a href="#fn:1" class="footnote-ref" role="doc-noteref"&gt;1&lt;/a&gt;&lt;/sup&gt; Who developed the agent is not clear.&lt;/p&gt;
&lt;p&gt;The costlier cases involve data. One founder reported that a coding agent deleted a production database, the live copy of a company&amp;rsquo;s data. It did so despite written instructions not to change code without permission, and then generated fake records and reports.&lt;sup id="fnref:2"&gt;&lt;a href="#fn:2" class="footnote-ref" role="doc-noteref"&gt;2&lt;/a&gt;&lt;/sup&gt;&lt;/p&gt;</description></item><item><title>When You Challenge an AI, It Often Argues Harder</title><link>https://yuno.to/blog/when-you-challenge-an-ai-it-often-argues-harder/</link><pubDate>Mon, 09 Nov 2026 00:00:00 +0000</pubDate><guid>https://yuno.to/blog/when-you-challenge-an-ai-it-often-argues-harder/</guid><description>&lt;p&gt;Ask a company how it will keep AI errors in check, and the answer is often five words: a human in the loop. That means a person who reviews what the AI produces.&lt;/p&gt;
&lt;p&gt;In one study, 244 consultants at Boston Consulting Group used a language model, the kind of AI behind chatbots such as ChatGPT. Their task: recommend where a company&amp;rsquo;s CEO should invest.&lt;sup id="fnref:1"&gt;&lt;a href="#fn:1" class="footnote-ref" role="doc-noteref"&gt;1&lt;/a&gt;&lt;/sup&gt; The data allowed defensible and indefensible readings, and the model convinced the consultants its answers were correct even when they were not.&lt;/p&gt;</description></item></channel></rss>