How to Deploy an MCP-Based AI Agent to Production Using Kubernetes and CI/CD

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Deploying a Model Context Protocol (MCP)-based AI agent beyond a local environment requires a structured production architecture covering containerization, orchestration, and observability. The proposed blueprint routes code through GitHub and GitHub Actions into a container registry, ultimately running on a Kubernetes cluster connected to MCP servers and external services. Docker is used to package the agent with a lightweight, non-root runtime, keeping credentials outside the image entirely. Kubernetes manages deployment, scaling, restarts, and rolling updates, while secrets management tools protect API keys and access tokens. Observability tooling — covering logs, metrics, traces, and alerts — provides the visibility needed to maintain reliability once real users and external dependencies are involved.

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