<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Integration on TCS Labs Academy</title><link>https://meshery-extensions.github.io/tcslabs-academy/pr-preview/pr-36/tags/integration/</link><description>Recent content in Integration 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/integration/index.xml" rel="self" type="application/rss+xml"/><item><title>Integrate</title><link>https://meshery-extensions.github.io/tcslabs-academy/pr-preview/pr-36/learning-paths/deea6061-b6be-49a9-ad1c-f1a5c32e1fa9/model-context-protocol-and-agentic-integrations-for-meshery/integrating-coding-agents-with-meshery/integrate/</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/model-context-protocol-and-agentic-integrations-for-meshery/integrating-coding-agents-with-meshery/integrate/</guid><description/></item><item><title>3. Integrating Coding Agents with Meshery</title><link>https://meshery-extensions.github.io/tcslabs-academy/pr-preview/pr-36/learning-paths/deea6061-b6be-49a9-ad1c-f1a5c32e1fa9/model-context-protocol-and-agentic-integrations-for-meshery/integrating-coding-agents-with-meshery/</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/model-context-protocol-and-agentic-integrations-for-meshery/integrating-coding-agents-with-meshery/</guid><description>&lt;p&gt;Modern platform engineering increasingly relies on coding agents to automate routine infrastructure tasks - reading cluster state, proposing design changes, and deploying workloads without manual intervention at every step. Meshery provides multiple programmatic surfaces - an MCP server, a REST API, a GraphQL API, and the &lt;code&gt;mesheryctl&lt;/code&gt; CLI - that a coding agent can combine to act as a capable infrastructure operator.&lt;/p&gt;
&lt;p&gt;This course walks through each surface in detail: how to authenticate and connect an agent, when to reach for GraphQL versus REST, and how to combine MCP tools with &lt;code&gt;mesheryctl&lt;/code&gt; for tasks that require both structured reads and imperative actions. The course concludes with a complete end-to-end workflow in which an agent reads live cluster state, proposes a design change, deploys it through an approval gate, and verifies the outcome - a pattern directly applicable to production platform automation.&lt;/p&gt;</description></item></channel></rss>