<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>LLM Engineering on XiDao 技术博客</title><link>https://blog.xidao.online/categories/llm-engineering/</link><description>Recent content in LLM Engineering on XiDao 技术博客</description><generator>Hugo -- gohugo.io</generator><language>zh-cn</language><copyright>© 2026 XiDao</copyright><lastBuildDate>Sun, 10 May 2026 09:00:00 +0800</lastBuildDate><atom:link href="https://blog.xidao.online/categories/llm-engineering/index.xml" rel="self" type="application/rss+xml"/><item><title>Multi-Model AI Agent Orchestration in 2026: Collaborating with Claude 4.7, GPT-5.5, and Gemini 2.5</title><link>https://blog.xidao.online/en/posts/multi-model-orchestration-2026/</link><pubDate>Sun, 10 May 2026 09:00:00 +0800</pubDate><guid>https://blog.xidao.online/en/posts/multi-model-orchestration-2026/</guid><description>&lt;p&gt;In 2026, AI Agents have moved from proof-of-concept to production deployment. The era of single-model solutions is fading, replaced by a new paradigm of multi-model collaborative orchestration. This article explores how to build high-performance multi-model AI Agent systems using Claude 4.7, GPT-5.5, and Gemini 2.5.&lt;/p&gt;</description></item></channel></rss>