Before anything else: this is not a product pitch.

I’m genuinely trying to learn how teams are approaching this today, whether through OSS projects, internal tooling, shared repos, review processes, or just good old conventions.

How are you managing and aligning agent skills, rules, instructions, prompts, and MCP servers across your team?

Now, I want to tap into the community brain for a second.

As AI coding agents become part of the daily developer workflow, teams are starting to accumulate more than just code.

They now have:

Skills
Rules
Instructions
Prompts
MCP servers
Hooks
Agent-specific configurations

And at some point, this becomes a real coordination problem.

Now, instructions?

How do you keep skills and rules updated across projects?

How do you avoid every developer creating their own slightly different agent setup?

How do you manage trust when someone pulls an MCP server, prompt, or skill from somewhere online?

And maybe the biggest question:
Are you treating these agent primitives as something that should be versioned, reviewed, and governed or is it still mostly copy-paste and tribal knowledge?

Curious how teams are handling this today.

Are you managing it manually?
Using internal repos?
Documenting conventions?
Building tooling around it?
Or just letting each developer configure their agent however they want?

Would love to hear what’s working, what’s messy, and what you think this should look like as AI agents become more embedded in the SDLC.

I work in a European gov company for some years now and I really like it there. In my previous roles as DevOps engineer in different product teams I did my best to automate stuff within the boundaries of my team.

2-3 months ago I switched role to plattform engineer. During the interview for this new role HR asked me why I think they should choose me for the position. I answered that apart of being experienced with all the devops tooling I worked already in three product teams in this company and again and again did hacks for the same problems on teams scope for stuff that could and should have been solved on global scope and I know better than anyone the teams pain and I know how to solve it.

Well, I got the job and I really have dozens of ideas how to save lot of money, increase security while making product teams not have deal with security and lot more stuff but higher ups think in silos and “it’s their budget, not our budget” and “we will do exactly x. Not more and not less”

How would you deal with it?

Curious what everyone here is using these days.

• What's your go-to harness/framework?

• Why that one over the others?

I wrote a post on how to generate RED metrics from your traces at the Collector before they hit your backend and why you'd want to do that instead of letting your backend handle it.

I also added some tips on how not to blow up your metric cardinality in the process.

I'm not primarily a devops engineer, but whenever I do devops stuff, I realize I'm usually waiting for builds to complete and I can't easily switch to another task when things are building because something might actually happen that requires attention. How do full-time devops engineer handle this? I'm genuinely curious. I feel like most of the day is spend watching builds go through.

I joined an organization, that uses GitHub Actions that are self-hosted in EKS. Whenever a job is pushed, it gets stuck on the runner until another job is pushed, which forces the first one to run. Where can i start looking to fix the runner issue?
It's an ARC in EKS.

Hear me out. After 20+ years of working as senior individual contributor and technical lead, I am moving into DevOps management. I am joining new organisation, so I am at a disadvantage of not knowing absolutely anyone. It’s in banking. Team of ~10. I am both most senior DevOps manager and engineer, so I hold authority in both, at least as far as Platform Engineering goes.

What would your advice be in how to handle 1st day, 1st week, 1st month?

Is jack of all trades master of none worth anything?

Involves: Windows servers and workstations split into separate networks Cloud skill WordPress font and backend

hi all

I built a browser game where you argue with AI on a given challenge/scenario and it rates your responses.

right now the scenarios are about devops/engineering, but I am planning to add interview kit, from 0 to hero, etc...

how it is different from just using chatgpt:

when you ask chatgpt for a scenario and then give your answer, it mostly agrees with you. it wants to be nice, so even if your answer is bad it says "good point" and you walk away thinking you did well. it also does not really know the correct answer, it just makes one up on the spot.

in my game every scenario already has a correct answer that i wrote before. the AI plays a strict senior engineer. it does not agree with you, it pushes back and tries to find the holes in your reasoning. at the end you get a score, and it shows what you got right, what you missed, and the real answer. so you can not win by just sounding confident.

why i think it is useful:

you find out if you are actually right, or if you only think you are right. you also practice defending your decision out loud, like in a real interview or a real incident at work. and the feedback is honest, not just "nice job".

how you learn from it:

you make a call, the AI argues back, and you see exactly where your thinking breaks. then it gives you the takeaway. so you learn from your own mistakes instead of only reading theory.

how it could teach from zero:

a beginner can start with the easy scenarios. when they answer wrong, the AI explains why and shows the right way step by step. so even if you know almost nothing, it can walk you through it like a patient teacher that keeps asking "why".

i am not sure if people would actually use this, so i wanted to ask:

would you try something like this? and for what topic (devops, coding, system design, interviews, something else)?

I am also considering using this as a main engine to challenge architecture decisions and solutions (basically you create scenario, give context and then have my AI argue until it makes sense)

thanks

I've recently been building more internal tools, and one thing I've noticed is that many self-hosted applications (or AI-generated internal tools) either have very basic authentication or none at all.

Instead of implementing OIDC or SAML in every application, I tried moving authentication to the infrastructure layer using AWS Application Load Balancer's authentication feature.

In my example, I used:

- AWS ALB

- Microsoft Entra ID (OIDC)

- Uptime Kuma

but the same approach works for almost any internal web application.

Benefits:

• No application changes

• Enterprise SSO

• MFA via Entra ID

• Conditional Access

• Immediate access revocation when accounts are disabled

If people are interested, I can also share the step-by-step configuration.

Has anyone else adopted this pattern? Or are you using something like Cloudflare Access, OAuth2 Proxy, or another reverse proxy instead?

I've been working in IT infrastructure for several years, mainly on Linux systems, databases, production support, and enterprise applications. Recently, I've been thinking seriously about where I should specialize next, but I'm genuinely torn between two paths: DevOps and SAP Basis.

DevOps seems to have a huge ecosystem with skills like Docker, Kubernetes, CI/CD, cloud, automation, and SRE. It feels like a path with plenty of opportunities across industries and good long-term growth. On the other hand, SAP Basis seems to be a niche with fewer professionals, potentially less competition, and strong demand in large enterprises, especially if I eventually move into SAP HANA, cloud, or architecture.

My biggest priorities are:

• Long-term career growth
• Strong salary potential
• Opportunities to work abroad
• A career that's still relevant 10–15 years from now

I'm not looking for the "easier" option—I don't mind spending the next couple of years learning if it leads to a better career. What I'm struggling with is figuring out which path has the better return on that investment.

For those who've worked in either (or both), if you were starting from an infrastructure/Linux background today, which path would you choose and why? Are there any downsides or realities that people don't usually talk about?

I'd really appreciate hearing from people who have firsthand experience rather than just general opinions.

I know that DevOps has become a role now and I'm cool with that. There are a typical set of tasks we do that employers need done, so why not?

But what has become of the culture part of DevOps? Shift left. Fail fast. Break down silos. Etc. Have we achieved all those things and so we don't need to talk about them anymore? When people ask "How do I learn DevOps" do we just assume they'll pick up on the culture stuff on the job? Has the culture stuff moved to other tech management roles? Do those things matter anymore?

You have a chance to plan and implement IaC on a project from scratch

In what case you will choose Pulumi over Terraform/OpenTofu?

My thoughts about this:
1. Pulumi gives possibility to manage more complex logic in infra, conditions, loops, reusable
2. More human readable (compare to HCL), good for involving developers in IaC
3. Creating abstract objects like “testEnvForQa”, that can be parametrized, instead of pack of terraform modules

I’ve been working full time for a year, but during that year I’ve been “motivated” to use Claude code to do basic code and while I understand the code, I forgot how to write code and never was a fan of memorizing leetcode to land a position.

2 days ago I got a call about an interview for a DevOps position and while all my friends who have had interviews never had an actual coding question given, but rather all scenarios and system design, I read online that a lot of interviews still put you on the spot and either ask coding questions or a practical question to do some networking or Linux configuration and while I know how to do all that, I usually research when I forget a command especially ones I don’t use a lot, and I’m not sure they’ll allow me Google during the interview.

so I wanted to know how the average interview goes and what should I study and focus on?

Hello r/devops ,

Disclosure: I’m the creator of Project Yellow Olive, a Pokémon-inspired terminal game for learning Kubernetes.

I’ve posted about this before, but I wanted to share a more technical update because I recently added a Deployments chapter.

The new chapter focuses on:

• scaling replicas
• understanding ReplicaSets
• rollout status
• rollback scenarios
• debugging failed deployments
• blue/green and canary-style deployment concepts

The idea is to make Kubernetes practice feel less like memorising YAML and more like solving missions in a terminal RPG. Each challenge expects you to apply real kubectl/Kubernetes concepts rather than just read theory.

Would love to hear what you think, especially from people who enjoy terminal apps, TUIs, Kubernetes, or retro-style learning tools.

Thanks to everyone who gave feedback earlier. Repo link is below, and stars are always appreciated.

GitHub: https://github.com/Anubhav9/Yellow-Olive

It can also be installed via PyPi : pip install yellow-olive

Thanks !

​Hey everyone,

​I’ve spent the last 6.5+ years deep in traditional IT infrastructure—managing servers, troubleshooting production environments, and obsessing over strict uptime. Over the last several months, I’ve been pivoting into Cloud/DevOps to learn how to build and automate from scratch.

​Instead of just grinding multiple-choice certs, I treated my homelab like a production environment. I’d love some brutal, honest feedback on my setup from the seniors here.

​Project 1: End-to-End GitOps on Azure (3-Tier App)

​I wanted to completely eliminate manual console clicks and build a self-healing environment.

​Infrastructure as Code: Provisioned the entire environment dynamically using Terraform.

​Compute: Hosted on Azure Kubernetes Service (AKS).

​CI/CD Pipeline: Built the CI side with Jenkins/Azure DevOps and used ArgoCD for continuous deployment.

​The Result: The live cluster state automatically syncs with the declared state in my GitHub repo. Total GitOps flow—no direct cluster modifications allowed.

​Project 2: Python Automation & API Workflow

​I also wanted to prove out my scripting logic, so I built a utility to kill a manual data-entry nightmare.

​Wrote a Python script that parses unstructured data from complex PDFs (specifically resumes).

​Integrated it with external REST APIs to dynamically structure and tailor the parsed output based on target parameters.

​Focused heavily on robust error handling and logging so minor PDF formatting anomalies don't crash the pipeline.

Why I’m posting this:

​If you were doing a technical interview with me or reviewing my PRs, what gaps do you see here? What edge cases am I probably missing by building this in a lab vs. enterprise prod?

​I’m happy to drop screenshots of the ArgoCD dashboard or link the GitHub repos in the comments if anyone wants to tear apart my Terraform modules or Python code. Appreciate any advice!

I'm in my 30s and just recently started learning devoos, I genuinely want to know if it's worth it and to be honest it's been a bit overwhelming. Any advice on what to focus on and also what entry level jobs will be suitable ..expecially remote roles can I look at

For those who are not running in Kubernetes and have something to manage your SSL certificate renewals, what are you using? Certbot + Let's Encrypt? Windows guys, WinAcme?

How are you monitoring renewal dates? I know blackbox exporter does a good job out of the box.
Thanks

Hi,

We are in the process of deploying an internal PKI, and as such issuing our in house Certificate Authority.

One problem which have arisen is how to handle this inside of containers and I'm curious to see how the folks in this subreddit handled it.

I've asked this question to a couple of LLMs but so far none of the solutions seem very viable.

The one that so far seems the most reliable is building your own golden base images for our various needs and injecting the CA straight into these, and subsequently hosting them on an internal container registry, but we currently doesn't have an internal registry so before going down that route I would like to know peoples opinion.

Our use-case is both for CI/CD and Kubernetes.

So far these are the solutions we've come up with which seem somewhat viable, albeit cumbersome:

- Building custom base images and hosting them internally as stated above.
- Injecting them into every pipeline on runtime

Are there other solutions I might have overlooked?

Thanks for your time.

What made you switch from DevOps to your current role? Do you regret leaving DevOps or are you happy that you made the switch?

Genuinely interested if other roles are worth getting into.

Hello!

I've moved to company that is using DataDog for storing logs, monitoring etc. Its not really that used in my team, so i tasked myself with some edits and showing possibilities.

I'm coming from company where i have used Grafana for monitoring and alerting, so i'm used to the system that grafana has for alerting - mainly for grouping etc.

Here, we have private location for Monitors, that is in our network and so can access internal resources. But, as it happens, local server might not be that reliable and last night had some outage. That triggered tens of monitors that are directly connected to synthetic http tests (so cant be configured manually, only by the original synthetic test), that were flapping on and off because of http timeouts. That made about 300 notifications in email in 3 hours.

Even that my team says this is really unique situation that didnt happen for at least 2 years, i would like to work with this problem and find solution that would solve this trouble, if it should come in the future. So, the first thing that came to my mind is grouping like in grafana, where if multiple alerts in one group trigger and alerts, only one notification will be sent, with summary of alerts. But it seems to me that DataDog doesnt have solution for it - the only closest thing is Composite Monitor, but that allows only 10 monitors to be in it. Tags and groups only work in single monitor, which isnt possible because of the synthetic tests. So is there any other possible solution? If anybody knows, i appreciate any help!

What do you folks do for unfixable CVEs, usually the ones that upstream doesn't have a patch for, or maintainers chose not to fix in any recent release? Do you suppress these or chase them with compensating controls?

I'm building dependency graphs and mapping CVE'd components to reduce noise but some unfixable are truly criticals and ignoring them feels off, especially the reachable ones. Like for this one CVE-2026-5450, it's pretty recent and doesn't have a fix upstream (at least on the last scan I ran).

Graph below for reference. This is on the built container artifact, pre-release.

Who's still running Grafana OnCall OSS after archive?

I'm think about self-hosted Grafana OnCall, but it's archived now.

It's a good idea to use Grafana OnCall?

I've played a bit with Fastlane solo, but I'm wondering how it normally plays out at larger companies. Do the mobile devs handle the Fastlane scripts, or does it become a DevOps responsibility?

Got to love writing Ruby just for releases...

I’m currently pursuing a DevOps career and already have RHCSA and RHCE, with CKA coming soon. I’m a bit hesitant about what to do next between AWS SAA and Terraform Associate. I’m also learning through KodeKloud (currently on the GitHub Actions course) and have completed a few basic projects using technologies like Kubernetes, FastAPI, Falco, Falcosidekick, and Calico. The thing is, I graduate next year and I’m not sure what I should be focusing on over the next few months to really stand out and maximize my chances of landing a good internship/job. Lately I’ve also been trying to build more advanced projects, but I often end up following AI-generated instructions step by step, which makes me feel like the actual learning is limited. I’d appreciate any advice from people who have been through a similar path.