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Platform Engineering With AI

5 min read
FullstackPlatform

Fullstack

Full-stack devs building internal tools: AI scaffolds; you own usability for your internal users.

Platform

IDP strategy, self-serve workflows, and developer happiness are human-led. AI implements.

Platform Engineering With AI

TL;DR

  • AI generates Terraform, K8s manifests, dev portals, and internal tooling. Implementation speed is high.
  • Platform engineering is about developer experience and operational clarity. AI doesn't own strategy.
  • Use AI for scaffolding and automation. You own: what to build, for whom, and why it matters.

Platform engineering is the discipline of making it easy for developers to ship. Internal developer platforms (IDPs), self-serve environments, golden paths, and infra-as-code. AI can generate a lot of that. It can't decide what your developers need, how to measure success, or how to evolve the platform as the org grows. That's you.

What AI Can Generate

  • Terraform / Pulumi / Crossplane. Modules, resources, wiring. AI knows the providers and patterns.
  • K8s manifests. Deployments, services, ingress, config maps. Boilerplate, AI handles it.
  • Dev portal UIs. Backstage plugins, custom dashboards. AI scaffolds React and API calls.
  • CI/CD pipelines. GitHub Actions, GitLab CI, Argo. AI generates from a description.
  • Documentation. READMEs, runbooks, API docs. AI drafts; you refine for accuracy and tone.

What AI Can't Decide

  • What to platform-ize. Should you offer self-serve K8s? Managed DBs? Feature flags? Depends on your team size and maturity.
  • Golden path design. What's the "right" way to deploy? AI implements; you define.
  • Developer feedback loops. What's confusing? What's slow? You talk to users. AI doesn't.
  • Operational boundaries. Who owns what? Platform vs. app team responsibilities. Organizational design, not code.
  • Cost and governance. Resource limits, quotas, approval flows. Business decisions.

The Platform Engineer's AI Workflow

  1. Define the experience. "Developers should be able to spin up a preview env in under 2 minutes." That's the goal.
  2. Use AI to scaffold. "Create a Terraform module for a preview environment with [specifics]." Get 80% of the config.
  3. Add the platform polish. Error messages, docs, guardrails. AI generates; you improve DX.
  4. Measure and iterate. Adoption, time-to-first-deploy, support tickets. AI doesn't run retrospectives.

Common AI Platform Mistakes

  • Over-engineering. AI suggests a full GitOps setup when a simple script would do. Match complexity to team size.
  • Generic defaults. AI uses standard resource limits and configs. Your org may need different.
  • Missing operational context. Runbooks, escalation, blast radius. AI generates templates; you add the reality.
  • Ignoring feedback. Platform success is measured by developer happiness. AI can't interview users.

Write Terraform by hand. Build dev portal UI from scratch. Document everything manually. Weeks for a new platform capability.

Click "Platform Engineering With AI" to see the difference →

# Define experience first, then scaffold
"Developers should spin up preview env in under 2 minutes"

# AI scaffold prompt
"Terraform module: preview env, isolated namespace,
resource limits, ingress for *.preview.<domain>.
Add clear error messages and link to runbook."

Quick Check

AI suggested a full GitOps setup for your 5-person team. What's the right move?

Do This Next

  1. List one pain point your developers have today. Draft a solution with AI (Terraform, script, UI). Then add: clear docs, error handling, and a feedback mechanism. The AI part is fast; the DX part is you.
  2. Document your platform principles: What do you optimize for? Speed? Safety? Consistency? Use it to evaluate every AI-generated platform change.