<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Architecting on FindPicked</title><link>https://findpicked.com/tags/architecting/</link><description>Recent content in Architecting on FindPicked</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sun, 05 Jul 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://findpicked.com/tags/architecting/index.xml" rel="self" type="application/rss+xml"/><item><title>Architecting Interoperable AI Agent Systems with MCP</title><link>https://findpicked.com/blog/mcp-for-multi-agent-systems/</link><pubDate>Sun, 05 Jul 2026 00:00:00 +0000</pubDate><guid>https://findpicked.com/blog/mcp-for-multi-agent-systems/</guid><description>&lt;p&gt;Building sophisticated &lt;strong&gt;AI agent&lt;/strong&gt; systems often hits a wall when agents need to communicate effectively with each other or integrate with diverse external tools and data sources. The &lt;strong&gt;Model Context Protocol (MCP)&lt;/strong&gt; emerges as a crucial open standard designed to overcome these interoperability challenges. This article explores how MCP facilitates robust communication, coordination, and integration among multiple AI agents and external systems, offering practical guidance and architectural patterns for developers tackling complex agentic workflows.&lt;/p&gt;</description></item><item><title>Architecting Resilient AI Agents for Production Safety</title><link>https://findpicked.com/blog/architecting-resilient-ai-agents/</link><pubDate>Fri, 03 Jul 2026 00:00:00 +0000</pubDate><guid>https://findpicked.com/blog/architecting-resilient-ai-agents/</guid><description>&lt;p&gt;As AI agents move from experimental prototypes to critical production systems, ensuring their safety and resilience becomes paramount. Unlike traditional software, these autonomous entities can plan, execute multi-step tasks, and interact with the real world through tools, introducing novel risks like data corruption, financial losses, or system compromises. This guide offers developers a comprehensive approach to designing, deploying, and operating &lt;strong&gt;AI agents&lt;/strong&gt; with robust, built-in safety mechanisms that go beyond simple guardrails.&lt;/p&gt;</description></item></channel></rss>