Platform Engineering
Platform engineering is how you turn repeated infrastructure work into a product other engineers can depend on. I have spent the last several years building the internal surfaces that let product teams ship without re-solving authentication, deployment, observability, and tenant isolation on every project.
The work is not about wrapping Kubernetes in a UI. It is about understanding where teams lose time - slow feedback loops, unclear ownership of shared services, secrets scattered across repos - and designing platforms that remove those bottlenecks with guardrails instead of gatekeeping.
On this page you will find writing on IDP design, benchmark suites for platform changes, policy-as-code, and how platform teams should evolve as AI agents enter the delivery stack. Each article comes from production constraints: multi-tenant SaaS, GitHub Actions at scale, and teams that need self-service without sacrificing reliability.
- Designing an Internal Developer PlatformAn IDP is golden paths and self-service guardrails-not just a portal. Map developer journeys, ship templates first, and measure DORA metrics plus developer satisfaction.
- Platform Engineering vs DevOpsDevOps is how teams collaborate on delivery; platform engineering productizes the infra layer so that collaboration scales. One did not replace the other.
- Designing Benchmark Suites for Platform ChangesGreen application CI does not catch an 18% P99 regression from a node image swap. How to build layered platform benchmark suites, statistical regression gates, and dedicated environments that validate substrate changes.
Related writing
Benchmarking
API Load Benchmarking for Platform Teams
Load tests that only report RPS miss the point. Platform teams need benchmarks that answer capacity questions, find breaking points before users do, and produce numbers engineering leadership can act on.
- Benchmarking
- APIs
- Performance
Automation
Automating Runbooks with GitHub Actions
Notion runbooks and 3 AM kubectl do not scale. A GitHub Actions pattern for reusable runbook workflows, OIDC to AWS, environment approval gates, and audit trails that on-call rotations can actually trust.
- GitHub Actions
- Automation
- SRE
Benchmarking
Designing Benchmark Suites for Platform Changes
Green application CI does not catch an 18% P99 regression from a node image swap. How to build layered platform benchmark suites, statistical regression gates, and dedicated environments that validate substrate changes.
- Benchmarking
- Performance
- Platform Engineering
CI/CD
Designing GitOps With Jenkins, GitHub Actions, and Argo CD
Most teams do not need one CI/CD tool to rule them all. After running Jenkins, GitHub Actions, and Argo CD together in production, the golden rule is simple: GHA for CI, Jenkins for specialized automation, Argo CD for Kubernetes CD.
- GitOps
- GitHub Actions
- Jenkins
Platform Engineering
Designing an Internal Developer Platform
An IDP is golden paths and self-service guardrails-not just a portal. Map developer journeys, ship templates first, and measure DORA metrics plus developer satisfaction.
- Platform Engineering
- Developer Experience
- CI/CD
Kubernetes
Designing a Production-Ready Kubernetes Platform
A production K8s platform is RBAC, ingress, secrets, observability, and GitOps-not just a cluster. Here is the layered checklist I use before onboarding product teams.
- Kubernetes
- Platform Engineering
- AWS
AI Infrastructure
Designing Reliable AI API Infrastructure
AI APIs fail differently from CRUD APIs - provider rate limits, token budgets, and unpredictable latency need a dedicated reliability layer. Here is how I design inference infrastructure on AWS that survives real traffic.
- AI
- APIs
- AWS
Reliability
Designing Zero-Downtime Deployments
Zero downtime needs compatible migrations, readiness probes, and graceful shutdown-then choose rolling, blue-green, or canary based on blast radius and rollback needs.
- CI/CD
- Kubernetes
- Reliability
AI Infrastructure
Evaluating AI Coding Agents in CI
HumanEval scores do not predict whether an agent will break your migrations. A fixture-based eval harness, autonomy tiers, and CI gates that treat agent patches with the same distrust as human ones.
- AI Agents
- CI/CD
- GitHub Actions
Platform Engineering
Platform Engineering in the Age of AI Agents
AI agents change what platform teams must provide - not just CI/CD and observability, but guardrails for autonomous, non-deterministic workloads. Here is how I expect platform engineering to evolve and what I am building toward today.
- Platform Engineering
- AI Agents
- Governance
Platform Engineering
Platform Engineering vs DevOps
DevOps is how teams collaborate on delivery; platform engineering productizes the infra layer so that collaboration scales. One did not replace the other.
- Platform Engineering
- DevOps
AI Security
Prompt Injection Defenses for Internal Agents
SSO does not stop an injected Jira ticket from triggering a production runbook. Defense layers for internal AI agents-tool allowlists, context separation, approval gates, and blast radius containment.
- AI Agents
- Security
- Platform Engineering
Security
Secrets Management for Platform Teams
Hardcoded API keys in .env files do not survive a team of ten, three environments, and a compliance audit. Here is the secrets management approach I implement for platform teams on AWS.
- Security
- AWS
- Platform Engineering
Observability
Slack Alerting That Scales with On-Call
Alert fatigue is a routing and enrichment problem, not a Slack problem. Tiered channels, Alertmanager as source of truth, runbook annotations, and alert budgets that scale with on-call rotations.
- Observability
- SRE
- Automation
CI/CD
Why Do We Need Argo CD If We Already Have Jenkins?
Jenkins runs CI workflows; Argo CD continuously reconciles Kubernetes from Git. They complement each other-confusing them leads to drift, risky credentials, and painful rollbacks.
- GitOps
- Kubernetes
- CI/CD