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Introduction

A US food manufacturer runs about 45 microservices on Kubernetes, on-premises and in the cloud, and the plant depends on them around the clock. XenonStack runs the platform as a 24x7 managed service: GitOps in place of manual releases, a standby cluster for failover, full monitoring, and a monthly review of every service restart that has cut restarts significantly.

Git Branch Feeding Kubernetes Clusters

Client

US food manufacturer

Industry

Food and dairy manufacturing

Service

24x7 managed Kubernetes

Scale

About 45 microservices

In brief

  • The client runs its plant and business applications as about 45 microservices on Kubernetes, on-premises and in the cloud, and needs them up around the clock.
  • XenonStack runs the platform day to day: GitOps and automated pipelines in place of manual releases, a standby cluster for failover, and full monitoring.
  • Every month the team reviews each service restart, finds the cause and fixes it. Restarts have fallen significantly since the reviews began.

The challenge

Manufacturing does not stop for software. When an application that supports production or orders goes down, the plant feels it. The client's platform had grown into dozens of microservices across on-premises and cloud Kubernetes, with releases managed from an older version-control set-up and little visibility into why services restarted.

The client wanted one team accountable for keeping the platform healthy, a safer way to ship changes, and a recovery path if a cluster failed.

What we run

1. GitOps and automated delivery

The desired state of every service lives in Git, and automated pipelines build, test and deploy it. Each change is reviewed, repeatable and easy to roll back.

2. Production and standby clusters

A standby cluster mirrors production so workloads can fail over if the primary environment has a problem. We test the failover path rather than assuming it works.

3. Monitoring the team acts on

Prometheus and Grafana track cluster and service health, uptime checks watch the endpoints users depend on, and alerts go to the 24x7 team. Message queues are monitored alongside the services that use them.

4. Agreed service levels

The service runs against defined uptime and response targets, with tiers the client chooses by how critical each application is.

Monthly root-cause reviews

Every month we report on each microservice restart: when it happened, what caused it and what we changed. Early reports surfaced repeat causes, such as data-serialisation errors and dropped database connections. Fixing those at the source, rather than restarting and moving on, has cut restarts significantly.

Security and resilience

Container scanning

We scan the images in the client's private container registry for known vulnerabilities and track fixes by severity.

Backup and recovery

Next step proposed: daily snapshots of cluster state, backups of message-queue data, and offsite, tamper-proof copies, so the platform can be rebuilt even after a serious incident.

Why it matters

Agentic AI runs on the same platforms that run the business today. A Kubernetes estate with GitOps, tested failover, root-cause discipline and clean images is the foundation that lets a manufacturer add AI agents to operations safely. It is also where site-reliability agents such as AgentSRE can take on monitoring and root-cause work next.

See how the stack fits together on the XenonStack ecosystem page and in ElixirClaw. To see what happens before you book, read how the Context Workshop works.

Map the first decision your agents should make, in a Context Workshop

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jagreet-gill

Dr. Jagreet Kaur

Chief AI Officer, XenonStack

"Agents can only run safely on a platform that is already disciplined. Every restart explained, every change in Git, and a cluster ready to take over: that is where AI in operations starts."

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