<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Sandboxes on FindPicked</title><link>https://findpicked.com/tags/sandboxes/</link><description>Recent content in Sandboxes on FindPicked</description><generator>Hugo</generator><language>en</language><lastBuildDate>Thu, 25 Jun 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://findpicked.com/tags/sandboxes/index.xml" rel="self" type="application/rss+xml"/><item><title>AI Coding Agent Guardrails: Safe-by-Design Guide</title><link>https://findpicked.com/blog/ai-coding-agent-guardrails/</link><pubDate>Thu, 25 Jun 2026 00:00:00 +0000</pubDate><guid>https://findpicked.com/blog/ai-coding-agent-guardrails/</guid><description>&lt;p&gt;AI coding agents can be useful in production workflows &lt;strong&gt;only if you treat them like untrusted automation with constrained power&lt;/strong&gt;. The safest approach is not “trust the model less” in the abstract, but to build concrete controls around &lt;strong&gt;permissions, budgets, sandboxes, approvals, logs, and rollback paths&lt;/strong&gt; so a bad prompt, tool bug, or prompt-injection attempt cannot turn into a repo-wide or account-wide incident.&lt;/p&gt;
&lt;p&gt;Recent discussion around agent security has made one thing clear: &lt;strong&gt;guardrails alone are not enough if they are easy to bypass, overly broad, or so strict that teams disable them&lt;/strong&gt;. This guide shows how to design practical, layered controls for AI coding agents without relying on vendor promises.&lt;/p&gt;</description></item></channel></rss>