<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Reasoning on EnRedAndo Me - Carlos Prados</title><link>https://carlos.enredando.me/tags/reasoning/</link><description>Recent content in Reasoning on EnRedAndo Me - Carlos Prados</description><generator>Hugo -- gohugo.io</generator><language>en</language><managingEditor>mail@carlosprados.com (Carlos Prados)</managingEditor><webMaster>mail@carlosprados.com (Carlos Prados)</webMaster><copyright>© 2026 Carlos Prados</copyright><lastBuildDate>Tue, 01 Sep 2026 09:00:00 +0200</lastBuildDate><atom:link href="https://carlos.enredando.me/tags/reasoning/index.xml" rel="self" type="application/rss+xml"/><item><title>Mastering Agentic AI: Reasoning Techniques</title><link>https://carlos.enredando.me/posts/agentic-ai-reasoning/</link><pubDate>Tue, 01 Sep 2026 09:00:00 +0200</pubDate><author>mail@carlosprados.com (Carlos Prados)</author><guid>https://carlos.enredando.me/posts/agentic-ai-reasoning/</guid><description>&lt;p&gt;In my &lt;a href="https://carlos.enredando.me/posts/agentic-ai-resource-aware/" &gt;previous post&lt;/a&gt;, we looked at Resource-Aware Optimization — teaching agents to spend tokens, latency, and money deliberately instead of burning the biggest model on every request. That pattern is about &lt;em&gt;how much&lt;/em&gt; to think. This one is about &lt;em&gt;how&lt;/em&gt; to think.&lt;/p&gt;
&lt;p&gt;Most LLM failures on hard problems aren&amp;rsquo;t knowledge failures. The model knows the facts. It just blurts out the answer in one shot, skips the intermediate steps, and gets the logic wrong. Ask it to compute compound interest or untangle a multi-hop question and it will confidently hand you a plausible-looking mistake.&lt;/p&gt;</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://carlos.enredando.me/posts/agentic-ai-reasoning/featured.jpg"/></item></channel></rss>