Productionizing an MCP-Based AI Agent with Docker, Kubernetes, CI/CD, and Observability

Short summary

A practical architecture blueprint for taking MCP-based AI agents from local development to production using Docker, Kubernetes, CI/CD, and observability tooling. It covers containerization best practices, Kubernetes deployment configs with readiness and liveness probes, secrets management, and the delivery flow from GitHub through Actions to a container registry and cluster. The article addresses operational challenges specific to AI agent workloads such as MCP server availability and credential management.

• •Outlines a full CI/CD pipeline from GitHub to Kubernetes for MCP-based AI agents
• •Provides Dockerfile and Kubernetes deployment examples with health checks and resource limits
• •Covers secrets management, observability, and rollback strategies for AI agent production workloads

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