<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Gitops on TCS Labs Academy</title><link>https://meshery-extensions.github.io/tcslabs-academy/pr-preview/pr-36/tags/gitops/</link><description>Recent content in Gitops on TCS Labs Academy</description><generator>Hugo</generator><language>en</language><atom:link href="https://meshery-extensions.github.io/tcslabs-academy/pr-preview/pr-36/tags/gitops/index.xml" rel="self" type="application/rss+xml"/><item><title>Deploying with Agents</title><link>https://meshery-extensions.github.io/tcslabs-academy/pr-preview/pr-36/learning-paths/deea6061-b6be-49a9-ad1c-f1a5c32e1fa9/ai-assisted-infrastructure-design-with-meshery-and-kanvas/agent-driven-deployment/deploying-with-agents/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://meshery-extensions.github.io/tcslabs-academy/pr-preview/pr-36/learning-paths/deea6061-b6be-49a9-ad1c-f1a5c32e1fa9/ai-assisted-infrastructure-design-with-meshery-and-kanvas/agent-driven-deployment/deploying-with-agents/</guid><description/></item><item><title>3. Agent-Driven Deployment</title><link>https://meshery-extensions.github.io/tcslabs-academy/pr-preview/pr-36/learning-paths/deea6061-b6be-49a9-ad1c-f1a5c32e1fa9/ai-assisted-infrastructure-design-with-meshery-and-kanvas/agent-driven-deployment/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://meshery-extensions.github.io/tcslabs-academy/pr-preview/pr-36/learning-paths/deea6061-b6be-49a9-ad1c-f1a5c32e1fa9/ai-assisted-infrastructure-design-with-meshery-and-kanvas/agent-driven-deployment/</guid><description>&lt;p&gt;Platform engineers rarely deploy by hand anymore - the same coding agents that generate and refine a Meshery design can also commit that design to Git, run a dry-run diff, and push it to a cluster once a human approves the output. This course walks through every stage of that loop with concrete commands and realistic scenarios.&lt;/p&gt;
&lt;p&gt;You will learn how to wire an agent into a GitOps pipeline so that &lt;code&gt;mesheryctl&lt;/code&gt; is the enforced execution path, how to read a dry-run diff and reason about blast radius before applying anything, and how to recover cleanly when a deployment goes wrong. The four lessons move in order from architecture to practice: the big-picture loop, safe preview workflows, a hands-on deployment walkthrough, and finally rollback and recovery.&lt;/p&gt;</description></item></channel></rss>