Building an AI agent locally is an exciting first step. Running that same agent reliably in production is a different challenge.
Once real users and external services are involved, the application needs more than working code. It needs repeatable deployments, secure configuration, health checks, monitoring, controlled updates, and a clear recovery process.
This article is part of my MCP series. If you are new to the topic, start with my first article: Model Context Protocol (MCP) Servers Explained: A Complete Beginner’s Guide.
In this article, I will outline a practical architecture for taking a Model Context Protocol, or MCP-based, AI agent from a local development environment to Kubernetes.
This is a production architecture blueprint. The exact implementation will depend on the AI provider, MCP servers, cloud platform, and security requirements used by the application.
