<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Financial Services on WhizMe.ai Tech Blog</title><link>https://www.whizme.ai/tags/financial-services/</link><description>Recent content in Financial Services on WhizMe.ai Tech Blog</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 08 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://www.whizme.ai/tags/financial-services/index.xml" rel="self" type="application/rss+xml"/><item><title>How AI Agents Improve Over Time in Global Financial Services</title><link>https://www.whizme.ai/blogs/how-ai-agents-improve-over-time-gfs/</link><pubDate>Fri, 08 May 2026 00:00:00 +0000</pubDate><guid>https://www.whizme.ai/blogs/how-ai-agents-improve-over-time-gfs/</guid><description>&lt;p>&lt;em>A practical guide to governed agent learning in regulated environments.&lt;/em>&lt;/p>
&lt;p>A question I often hear from financial-services customers is:&lt;/p>
&lt;blockquote>
&lt;p>How do AI agents improve over time through interactions with customers, users, and feedback data?&lt;/p>
&lt;/blockquote>
&lt;p>The short answer is: &lt;strong>not by secretly retraining themselves on live customer conversations&lt;/strong>.&lt;/p>
&lt;p>In a regulated financial-services environment, the foundation model remains fixed during inference. What improves over time is the &lt;strong>agent system around the model&lt;/strong>: the context it receives, the memory it is allowed to retain, the knowledge it retrieves, the skills and tools it can invoke, the evaluations used to measure it, and the governed release process used to update it.&lt;/p></description></item></channel></rss>