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.  

Subscribe to Kubernetes Bytes for conversations about Kubernetes, cloud native infrastructure, platform engineering, AI, security, and the CNCF ecosystem.

We Built an AI Developer Platform on Kubernetes

Kubernetes usually handles failures and rescheduling by restarting workloads. But what if Kubernetes could save the exact running state of a workload, move it somewhere else, and resume from where it stopped?  

In this episode of Kubernetes Bytes, Bhavin sits down with Radostin Stoyanov, maintainer of CRIU (Checkpoint/Restore In Userspace) and a contributor helping lead the Kubernetes Checkpoint/Restore Working Group.  

They explore how checkpoint/restore works, why it is becoming increasingly relevant for Kubernetes and AI workloads, and how the community is working toward pod-level checkpoint and restore.  

The conversation covers how checkpointing can help reduce expensive AI inference cold starts, improve fault tolerance for large GPU training jobs, enable faster model swapping, and potentially influence future Kubernetes scheduling and preemption workflows. 

They also discuss the challenges of checkpointing distributed applications, GPU memory, compression, security, encryption, and integrating checkpoint/restore directly into Kubernetes APIs.  

Topics include: 

 * What checkpoint/restore actually saves
 * Application-level vs. infrastructure-level checkpointing
 * CRIU and Kubernetes
 * Why AI inference workloads can take minutes to initialize
 * Saving CPU and GPU execution state 
 * Pod-level vs. container-level checkpointing 
 * Kubernetes Checkpoint/Restore Working Group 
 * Distributed checkpoint/restore 
 * Fault tolerance for large GPU training jobs 
 * Accelerating model startup and model swapping 
 * Memory compression for large checkpoints
 * Kubernetes APIs and controllers for checkpoint/restore 
 * Security risks of checkpoint files 
 * Encryption and validation 
 * Scheduler preemption and workload migration 
 * Checkpoint/restore for AI-agent sandboxes  



If you work with Kubernetes, GPUs, AI infrastructure, inference, distributed training, or scheduling, this episode provides a look at a Kubernetes capability that could become increasingly important as workloads become more expensive to restart.  

Subscribe to Kubernetes Bytes for conversations about Kubernetes, cloud native infrastructure, AI, platform engineering, security, and the CNCF ecosystem.

What if Kubernetes Could Resume Instead of Restart? | Checkpoint / Restore Explained

AI agents can do far more than answer questions — they can call tools, access data, execute actions, and act on behalf of users. But once agents start taking actions, an important question emerges: who is the agent actually acting as, and what should it be allowed to do?

In this episode of Kubernetes Bytes, Bhavin sits down with Maia Iyer, Research Software Engineer at IBM Research, to explore identity, authentication, authorization, and zero trust for AI agents.

They discuss why static API keys and simply passing user credentials to agents can create security problems, and how existing standards such as OAuth 2.0, SPIFFE, SPIRE, and Keycloak can help establish verifiable user and workload identities.

Maia also explains the work her team is doing around Rosso CTL and Cortex, including using a runtime layer around agents to handle identity, observe inbound and outbound interactions, apply guardrails, and explore intent-based access control.

Topics covered include:

• The building blocks of agentic applications
• Security anti-patterns for AI agents
• Static credentials and over-permissioned agents
• User identity vs. workload identity
• OAuth 2.0 and delegated identity
• SPIFFE and SPIRE
• Keycloak
• Rosso CTL and Cortex
• Intent-based access control
• Prompt injection and unexpected tool calls
• MCP and agent-to-tool communication
• Stateful AI agents
• Safely running agents that generate and execute code

If you're building AI agents on Kubernetes or thinking about how identity and authorization need to evolve for agentic systems, this episode provides a practical mental model for getting started.

Subscribe to Kubernetes Bytes for conversations about Kubernetes, cloud native infrastructure, platform engineering, security, AI infrastructure, and the CNCF ecosystem.

Your AI Agent Has Too Much Access | Fixing Agent Identity

In this episode of the Kubernetes Bytes podcast, Bhavin talks to Pushkar, Staff Software Engineer at Snap Inc. The discussion starts by talking about how platform teams support developers by building internal developer platforms, what are the capabilities developers expect in an IDP. They then talk about how reusing a Continuous Integration tool for Continuous Deployment led to issues and pagerduty alerts. Finally the discussion focusses on Argo Rollouts and the benefits it offers for rolling out updates to your production environment. This is a real world scenario where Pushkar shares lessons learnt, best practices, etc. from his experience onboarding Argo inside their organization.    

Check out our website at https://kubernetesbytes.com/  

Show Notes:

 * https://argoproj.github.io/rollouts/ 
 * https://argo-cd.readthedocs.io/en/stable/ 
 * https://buildkite.com/home/

Your CI Tool is NOT a CD Tool!

In this episode of the Kubernetes Bytes podcast, Bhavin talks to Phil Andrews, Global Field CTO at Cast AI about all things AI. The discussion starts by talking about why you don't need an Opus class or Fable/Mythos class model for each task, and how Kimchi from Cast AI can help manage the quality of your output with the cost associated with routing between different models. They also talk about Omni which allows customers to get GPUs across hyperscalers and neocloud providers. Listen to learn more! 

Check out our website at https://kubernetesbytes.com/  

Show Notes:

 * https://www.linkedin.com/in/philip-andrews-iii/
 * https://cast.ai/kimchi/
 * https://docs.cast.ai/docs/omni-overview 
 * https://cast.ai/blog/

Optimizing AI spend with Cast AI

In this episode of the Kubernetes Bytes podcast, Bhavin talks to Alex Zenla, Founder and CTO of Edera. The discussion starts by talking about why Alex and team decided to start Edera, where in the stack does it fit in, and how can it work with existing Kubernetes clusters. They also talk about GPU security, what Kubernetes DRA is, and how to plan for the new era of Mythos-class models. Listen to learn more! 

Check out our website at 

 * https://kubernetesbytes.com/    

Show Notes: 

 * https://www.linkedin.com/in/azenla/
 * https://edera.dev/
 * https://github.com/azenla 
 * https://edera.dev/stories

Securing Kubernetes for the Mythos Era

In this episode of the Kubernetes Bytes podcast, Bhavin talks to Rob Zuber, CTO at CircleCI about all things Software Delivery. The discussion starts by talking about the evolution of Software Delivery over the last decade, but then dives into how Vibe coding impacts CI pipelines. Rob also shares his insights about running an AI-first engineering organization at CircleCI. Listen to learn more! 

Check out our website at https://kubernetesbytes.com/  

Show Notes:

 * https://www.linkedin.com/in/robzuber/
 * https://circleci.com/
 * https://circleci.com/blog/

More Code, More Problems: Software Delivery in the Agentic Era

In this episode of the Kubernetes Bytes podcast, Bhavin talks to Anthony Woods about all things Grafana Labs, Observability, Telemetry, and how AI impacts both of these ecosystems. The discussion starts off by talking about the early days of Grafana Labs, what is Adaptive Telemetry, and how AI plays a role both in building Observability capabilities in applications, and how it helps perform root cause analysis. Listen to learn more!   

Check out our website at https://kubernetesbytes.com/      

Show Notes:  

 * GrafanaCON 2026: https://youtube.com/playlist?list=PLDGkOdUX1UjoSfz1IRj5c0xetw8tl8iin&si=JpT85m4t4bP8ZXgX  
 * Grafana Labs Blog: https://grafana.com/blog/  
 * https://www.linkedin.com/in/anthonywoods1/

Building Grafana Labs and the Future of Observability with Anthony Woods

In this episode of the Kubernetes Bytes podcast, Ryan and Bhavin talk to Janakiram MSV about all things KubeVirt. The discussion starts off by talking about the need for KubeVirt and then dives into the details of the KubeVirt architecture, and what you need to do when deploying virtual machines on Kubernetes, including commercial solutions like Red Hat OpenShift Virtualization and SUSE Virtualization.   

Check out our website at https://kubernetesbytes.com/    

Show Notes: 

 * https://www.youtube.com/watch?v=DDwfPuWlsl0 
 * https://janakiram.com/resources/ 
 * https://kubevirt.io/

Kubernetes for VMware Admins: Understanding KubeVirt

Join Ryan Wallner and Bhavin Shah for season 6 of the Kubernetes Bytes podcast. In this episode Bhavin talks to Adrian Mouat, Dev Rel at Chainguard about all things Kubernetes Security. They discuss CVEs, the different vulnerability databases, and how platform engineers can use Chainguard images to protect against CVEs.    Links: 

 * https://www.chainguard.dev/
 * https://www.linkedin.com/in/adrianmouat/ 
 * https://slsa.dev/

Secure your Kubernetes applications with Chainguard

Kubernetes Bytes (@kubernetesbytes) · Zencastr

Kubernetes Bytes (@kubernetesbytes)

Kubernetes Bytes is a podcast bringing you the latest from the world of cloud native data management. Hosts Ryan Wallner and Bhavin Shah come to you from Boston, Massachusetts with experienced backgrounds in cloud-native tech. They'll be sharing their thoughts on recent cloud native news and talking to industry experts about their experiences and challenges managing the wealth of data in today's cloud-native ecosystem.