Every (Agents) Connection to Railway
We've created a few workflow patterns on how to get your agent talking to Railway peacefully. The balance of Local and Remote MCPs, Railway's Agent Skills, the CLI + Railway Agent give your local harness all the ways to ride the rails and building in Railway.
Agents in the Sandbox
This week we shipped the most popular agent harnesses as default options in Railway Sandboxes. Once configured with your credentials, you can fire off prompts against Claude Code, OpenAI Codex, OpenCode, and Pi, right alongside all the parts of the applications your building. The agent loop just got faster!
State of Railway: Agents
Railway is turning the full deploy loop into something agents can actually drive: discovering Railway, signing up, shipping the first deploy, debugging failures, iterating in sandboxes, and handing off platform work to Railway Agent. We cover the agent-first CLI, local and remote MCP, Railway skills, infrastructure primitives, and Guardrails that let developers give agents more room to operate without losing control.
AI
Skill Issue, Writing /skills to solve agent failures
How we wrote the Railway Agent skills, which give agents repeatable workflows to follow.
Less Dashboards. More Robots. Railway, for Agents
We’re building the Agent Experience in Railway to be the easiest way to go from code running on your laptop, to the full stack up and running. One command, fully set up across the Railway CLI, MCP, and Railway’s agent skills. Ready to loop.
Railway MCP - Stateful, Serverful, Pay-per-use Infrastructure
We built a Railway MCP server that lets AI coding agents deploy apps and manage infrastructure directly from your code editor.
