/builds/openshift-paas
// where the reliability pattern came from
Restricted self-managed platforms running high-consequence workloads. Every dependency reviewed, every image scanned, every certificate managed deliberately. This is where the transferable reliability patterns came from: observability, deterministic delivery, graceful failure.
> The same principles that make infrastructure boring make AI reliable.
// patterns that transfer
One config file defines truth. Everything else renders from it. No manual editing, no drift. AI agents need this too: explicit context in, predictable behavior out.
Same inputs, same outputs. Every time. The foundation of trust, whether you're deploying containers or generating code.
If you can't see what's happening, you can't fix it. Logs, metrics, traces, the same visibility AI workflows need to debug when things go wrong.
Where AI meets infrastructure directly. Multi-generation GPU scheduling for ML teams running inference at scale. OpenShift with and without Run:AI.
// building trust in hostile environments
Air-gapped environments break assumptions. Every trust relationship has to be explicit. AI has the same problem, you can't assume anything works until you verify it.
Every dependency audited. No public registries. Every container image vendored, every chart pinned, every binary scanned. Trust nothing by default. The same principle behind vibe-check: measure before you trust.
Explicit trust chains. Internal PKI everywhere. Internal certificate chains, managed trust stores. No implicit trust, the same pattern AI agents need for authentication and authorization.
No easy escalation path. When something breaks, you learn from docs, source code, logs, and the system itself. That habit transfers directly to debugging AI delivery failures.
// the pattern
Environment-specific config. IPs, hostnames, feature flags. One file per site, overlays compose the final state.
Opinionated wrappers around upstream charts. Pinned versions, enterprise defaults, sync-wave ordering baked in.
Config flows one direction. Sites customize, base charts deploy.
// outcomes
Upgrades are boring.That's the goal. Change the version in config, let the pipeline render, watch dashboards. Platform upgrades shouldn't be events.
No tribal knowledge. Everything lives in Git. New engineers can trace exactly what happens from config change to deployed state. The pipeline is the documentation.
Developers stay unblocked.Self-service means the platform team isn't a bottleneck. Request infrastructure through Git, get infrastructure through Git.
// stack
The platform is the foundation. The tooling is what makes it operable: /builds/platform-tooling.
These patterns now apply to AI delivery, the 12-Factor AgentOps methodology maps every pattern on this page to agent equivalents.
> Start with the patterns that already work.