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What does it actually look like when a platform team starts using AI agents in production engineering workflows?
In this episode of Kubernetes Bytes, we sit down with Berkay from Futureplay to talk about how a small infrastructure and server engineering team is using Kubernetes to support mobile games, internal developer platforms, and increasingly AI-assisted software development.
The conversation starts with Futureplay’s Kubernetes architecture and its philosophy of keeping infrastructure lean and avoiding unnecessary complexity. Berkay explains how the team uses a decision journal to evaluate technology choices, how they decide between managed services and self-hosting, and how they provide self-service environments for developers.
Then we get into how AI is changing their engineering workflow.
Futureplay has built an internal system where users can submit requests through Slack, an orchestrator interprets and scopes the request, and Kubernetes Jobs spin up specialized AI workers for building, evaluating, and verifying the change.
The system can ask clarifying questions, create code, open a pull request, deploy a sandbox preview, collect feedback, and iterate — while still keeping human approval and security boundaries in place.
Topics include:
- Why Futureplay chose Kubernetes
- Keeping Kubernetes infrastructure lean
- Buy vs. build decisions for databases and infrastructure
- Developer self-service and ephemeral environments
- Using AI for Terraform, Helm, CI/CD, and infrastructure changes
- Why AI-generated production changes still require validation
- Agentic software development with Codex and Claude Code
- From Slack requests to pull requests and live previews
- Running AI workers as Kubernetes Jobs
- Building an in-house AI orchestrator
- Agent isolation, network policies, identity, and permissions
- Why Futureplay uses a dedicated cluster for AI workloads
- Why they currently choose hosted models over self-hosted GPUs
- Measuring platform engineering success
- Running a small team with near-five-nines availability
If you’re trying to understand what practical AI adoption looks like inside a real engineering organization — beyond demos and vendor promises — this episode offers a detailed look at one team’s evolving approach.
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