Blog
Notes on platform engineering, Kubernetes reliability, AI infrastructure, and production automation - written for engineers who ship systems, not slides.
Service Mesh: When You Need It and When You Do Not
Istio demos look effortless at fifteen services. A production decision framework for mesh adoption-signals that justify sidecars, alternatives that do not, and incremental rollout without cluster-wide regret.
- Kubernetes
- Networking
- Architecture
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
- Jenkins
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
- 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
Kubernetes CrashLoopBackOff: Production Debugging Guide
CrashLoopBackOff is a symptom, not a diagnosis. Use kubectl describe, logs --previous, and deployment diffs to find OOM, config, probe, and dependency failures fast.
- Kubernetes
- Debugging
Cost-Aware Autoscaling on Kubernetes
HPA, VPA, and Cluster Autoscaler fight each other when requests are wrong and metrics lie. A production playbook for scaling signals, instance mix, and scale-down discipline that protects SLOs without inflating the EKS bill.
- Kubernetes
- Cost
- AWS
How to Debug a Slow API in Production
When P99 spikes but errors stay flat, binary-search the request path with metrics and traces-starting at the edge, through the app, and into MySQL, Redis, and downstream dependencies.
- Observability
- SRE
- APIs
Logs vs Metrics vs Traces
Logs tell you what happened, metrics how much and how fast, traces where time went. Use all three with correlation IDs-alert on metrics, debug with logs and traces.
- Observability
- SRE
- OpenTelemetry
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
Observability for LLM Applications
Standard APM tells you the request was slow - it does not tell you why the model hallucinated or which prompt version caused a quality regression. Here is the observability stack I use for LLM applications in production.
- Observability
- AI
- OpenTelemetry
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
Webhook Idempotency in Production
Payment providers like Razorpay, Stripe, and PayU will deliver the same webhook more than once. If your handler is not idempotent, you will double-charge, double-enroll, or double-ship. Here is the pattern I use in production.
- APIs
- Reliability
- Payments
Chaos Engineering for Platform Reliability
Uptime during quiet weeks is not proof of resilience. A hypothesis-driven chaos playbook-tooling choices, blast radius controls, and the path from staging game days to bounded production experiments.
- Reliability
- SRE
- Kubernetes
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
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
- Platform Engineering
AI Agent Infrastructure and Failure Modes
AI agents combine LLM unpredictability with tool execution and long-running state - which creates failure modes traditional APIs never had. These are the ones I have seen in production and how I design around them.
- AI Agents
- Reliability
- Security
LLM Gateway Architecture
An LLM gateway is the control plane between your applications and model providers. Here is the architecture I use to centralize routing, rate limiting, cost accounting, and observability for every inference call.
- AI
- Architecture
- APIs
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
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
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
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
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
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
Policy as Code for Kubernetes Gatekeeping
Wiki standards do not stop :latest tags and root containers. A Gatekeeper and Kyverno playbook for admission control, audit-then-enforce rollouts, and CI integration that actually changes developer behavior.
- Kubernetes
- Security
- Policy