<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Best Practices on The Coders Blog</title><link>https://thecodersblog.com/tag/best-practices/</link><description>Recent content in Best Practices on The Coders Blog</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Wed, 06 May 2026 22:26:05 +0000</lastBuildDate><atom:link href="https://thecodersblog.com/tag/best-practices/index.xml" rel="self" type="application/rss+xml"/><item><title>Google Dev: Production-Ready AI Agents: 5 Lessons from Monolith Refactoring</title><link>https://thecodersblog.com/refactoring-monoliths-for-production-ai-agents-2026/</link><pubDate>Wed, 06 May 2026 22:26:05 +0000</pubDate><guid>https://thecodersblog.com/refactoring-monoliths-for-production-ai-agents-2026/</guid><description>&lt;p&gt;The dream of seamless AI automation is often sold as a flick of a switch. But the reality of deploying AI agents in production, especially when migrating from legacy monoliths, is a complex dance of architecture, resilience, and rigorous oversight. Forget brittle prototypes; we&amp;rsquo;re talking about robust, scalable systems. Google&amp;rsquo;s recent experiences, particularly from their &amp;ldquo;AI Agent Clinic,&amp;rdquo; offer a hard-won blueprint. Here are five critical lessons learned from refactoring monoliths to truly power production-ready AI agents.&lt;/p&gt;</description></item></channel></rss>