Most platform teams don’t fail because they lack tools. They fail because they automate the wrong things too early.
You’ve probably seen this play out. A team spends six months building a pristine internal developer platform, complete with golden paths, custom CLIs, and dashboards nobody uses. Meanwhile, developers are still waiting 45 minutes for builds, filing tickets for access, and copy-pasting YAML between repos.
Automation, in this context, is not about elegance. It’s about removing friction where it compounds.
So the real question isn’t “what can we automate?” It’s: what is currently slowing developers down every single day?
Let’s break that down properly.
What “Automation” Actually Means in Platform Engineering
Before prioritizing, it helps to define the scope.
Platform engineering automation is about standardizing and codifying workflows that developers repeat, so they become self-service, reliable, and fast. Think less “scripts everywhere” and more repeatable systems with guardrails.
Done right, it reduces cognitive load and cycle time. Done wrong, it creates brittle abstractions nobody trusts.
There’s a useful parallel here with SEO: optimizing a single page does little unless it fits into a broader system of internal links and structure. The same applies to platform automation. Isolated scripts don’t move the needle; systems do.
What Experts Are Quietly Agreeing On
We looked at how leading platform teams and practitioners prioritize automation, and there’s a pattern.
Nicole Forsgren, Partner at Microsoft and DORA co-author, consistently emphasizes that elite teams focus on deployment frequency and lead time first, not tooling sophistication. Translation: automate the path to production before anything else.
Kelsey Hightower, former Google Distinguished Engineer, has long argued that platforms should remove “toil,” defined as repetitive, manual work that scales linearly with growth. If a human has to click it every time, it’s a candidate.
Charity Majors, Honeycomb CTO, pushes a sharper point: if your developers need to file tickets to get work done, your platform is already failing. Automation should eliminate handoffs.
Put together, the signal is clear:
Start where friction is highest and most frequent, not where architecture is most interesting.
The Four Layers of Automation (Prioritized by Impact)
If you map platform work, most automation falls into four layers. The order matters.
| Layer | What it Covers | Impact if Automated |
|---|---|---|
| 1. Delivery | CI/CD, builds, deployments | Immediate speed gains |
| 2. Environment | Provisioning, infra, configs | Removes bottlenecks |
| 3. Access | Permissions, secrets, auth | Eliminates ticket queues |
| 4. Observability | Logs, metrics, tracing | Improves debugging speed |
Most teams try to start at layer 4 because it feels sophisticated. The high-leverage move is starting at layer 1.
Automate the Path to Production First
This is the highest ROI move, and it’s not close.
If your developers cannot reliably and quickly get code into production, everything else is noise.
In practical terms, this means:
- Standardized CI pipelines across repos
- One-click or automated deployments
- Built-in testing gates
- Rollback mechanisms that actually work
Here’s a simple example.
If a team deploys 20 times per week, and each deployment requires:
- 10 minutes of manual steps
- 5 minutes of context switching
That’s 300 minutes weekly, or 5 hours per team.
Now multiply that across 10 teams. You’re burning 50 hours per week on something that should be invisible.
Automation here doesn’t just save time. It increases deployment frequency, which DORA research ties directly to performance.
Pro tip: Don’t over-engineer pipelines early. Standardize 80 percent of use cases, allow escape hatches for the rest.
Kill Environment Setup Friction (Before It Kills Velocity)
The second biggest source of pain is environmental inconsistency.
You’ve seen it:
- “Works on my machine.”
- Local setup docs that are 47 steps long
- Dev, staging, and prod are behaving differently
Automation here means:
- Infrastructure as Code (Terraform, Pulumi)
- Ephemeral environments per PR
- Pre-configured dev environments (Dev Containers, Nix, etc.)
The goal is simple: a new engineer should go from zero to running code in under 30 minutes.
Anything longer is a tax on every new hire.
There’s a stronger effect, too. Consistent environments reduce bugs, which reduces firefighting, which frees up engineering time for actual product work.
Eliminate Access Tickets Entirely
This is the most underestimated bottleneck.
If developers need to:
- Request database access
- Wait for cloud permissions
- Ask for secrets
…your system is fundamentally slow.
Automation here looks like:
- Role-based access control (RBAC) with self-service flows
- Temporary credentials (just-in-time access)
- Secret management systems (Vault, AWS Secrets Manager)
Remember what Charity Majors implies: every ticket is a context switch and a delay.
Even if each ticket only takes 15 minutes to resolve, the real cost is the interruption to the flow.
Build Observability as a Default, Not an Afterthought
Only after the first three layers are solid should you invest heavily here.
Observability automation includes:
- Auto-instrumented services
- Standard logging formats
- Pre-built dashboards per service
- Alerting tied to real user impact
The key insight: developers should not have to “add observability” manually every time.
Treat it like internal linking in SEO. When systems are interconnected by default, discovery and debugging become dramatically easier.
How to Decide What to Automate Next (A Practical Framework)
If you’re unsure where to start, use this filter:
- Frequency – How often does this task happen?
- Friction – How painful is it when done manually?
- Blast radius – How many developers are affected?
Focus on tasks that score high on all three.
A good shortlist often looks like:
- Deployments
- Environment provisioning
- Access management
- Dependency updates
Not dashboards. Not internal portals. Not shiny abstractions.
FAQ
Should we build an internal developer platform first?
No. Start with automating workflows. A platform should emerge from proven patterns, not precede them.
What tools should we use?
Tooling matters less than consistency. GitHub Actions, GitLab CI, ArgoCD, Terraform, and Backstage are all valid. The mistake is switching tools instead of fixing workflows.
How much standardization is too much?
If developers are bypassing your system, you’ve gone too far. Provide defaults, not constraints.
Can small teams benefit from this?
Yes, arguably more. Small inefficiencies compound faster when you have fewer people.
Honest Takeaway
If you remember one thing, make it this:
Automate the things developers do every day, not the things platform engineers find interesting.
Start with delivery pipelines. Then fix environments. Then eliminate access friction. Only then worry about observability polish.
This isn’t glamorous work. It won’t make for flashy demos.
But it’s the difference between a platform that looks good on slides and one that developers actually rely on.
A seasoned technology executive with a proven record of developing and executing innovative strategies to scale high-growth SaaS platforms and enterprise solutions. As a hands-on CTO and systems architect, he combines technical excellence with visionary leadership to drive organizational success.
