<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Applications on Chen Kai Blog</title><link>https://www.chenk.top/en/tags/applications/</link><description>Recent content in Applications on Chen Kai Blog</description><generator>Hugo</generator><language>en</language><lastBuildDate>Mon, 19 Jan 2026 09:00:00 +0000</lastBuildDate><atom:link href="https://www.chenk.top/en/tags/applications/index.xml" rel="self" type="application/rss+xml"/><item><title>AI Agents Complete Guide: From Theory to Industrial Practice</title><link>https://www.chenk.top/en/standalone/ai-agents-complete-guide/</link><pubDate>Mon, 19 Jan 2026 09:00:00 +0000</pubDate><guid>https://www.chenk.top/en/standalone/ai-agents-complete-guide/</guid><description>&lt;p>A chatbot answers questions. An &lt;em>agent&lt;/em> gets things done — it browses, runs code, calls APIs, queries databases, and iterates until the job is complete. The same LLM powers both, but the wrapper differs: an agent runs in a loop with tools, memory, and the ability to inspect its own work.&lt;/p>
&lt;p>This guide is the expanded version of that idea. It covers the four core capabilities (planning, memory, tool use, reflection), major framework families, multi-agent collaboration, evaluation, and the production concerns that determine whether an agent succeeds or fails.&lt;/p></description></item></channel></rss>